Cellpose API Guide

Cellpose class

class cellpose.models.Cellpose(gpu=False, model_type='cyto', net_avg=True, device=None, torch=True, model_dir=None, omni=False)

main model which combines SizeModel and CellposeModel

Parameters

• gpu (bool (optional, default False)) – whether or not to use GPU, will check if GPU available

• model_type (str (optional, default 'cyto')) – ‘cyto’=cytoplasm model; ‘nuclei’=nucleus model

• net_avg (bool (optional, default True)) – loads the 4 built-in networks and averages them if True, loads one network if False

• device (gpu device (optional, default None)) – where model is saved (e.g. mx.gpu() or mx.cpu()), overrides gpu input, recommended if you want to use a specific GPU (e.g. mx.gpu(4) or torch.cuda.device(4))

• torch (bool (optional, default True)) – run model using torch if available

eval(x, batch_size=8, channels=None, channel_axis=None, z_axis=None, invert=False, normalize=True, diameter=30.0, do_3D=False, anisotropy=None, net_avg=True, augment=False, tile=True, tile_overlap=0.1, resample=True, interp=True, cluster=False, flow_threshold=0.4, mask_threshold=0.0, cellprob_threshold=None, dist_threshold=None, diam_threshold=12.0, min_size=15, stitch_threshold=0.0, rescale=None, progress=None, omni=False, verbose=False, transparency=False, model_loaded=False)

run cellpose and get masks

Parameters

• x (list or array of images) – can be list of 2D/3D images, or array of 2D/3D images, or 4D image array

• batch_size (int (optional, default 8)) – number of 224x224 patches to run simultaneously on the GPU (can make smaller or bigger depending on GPU memory usage)

• channels (list (optional, default None)) – list of channels, either of length 2 or of length number of images by 2. First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). For instance, to segment grayscale images, input [0,0]. To segment images with cells in green and nuclei in blue, input [2,3]. To segment one grayscale image and one image with cells in green and nuclei in blue, input [[0,0], [2,3]].

• channel_axis (int (optional, default None)) – if None, channels dimension is attempted to be automatically determined

• z_axis (int (optional, default None)) – if None, z dimension is attempted to be automatically determined

• invert (bool (optional, default False)) – invert image pixel intensity before running network (if True, image is also normalized)

• normalize (bool (optional, default True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

• diameter (float (optional, default 30.)) – if set to None, then diameter is automatically estimated if size model is loaded

• do_3D (bool (optional, default False)) – set to True to run 3D segmentation on 4D image input

• anisotropy (float (optional, default None)) – for 3D segmentation, optional rescaling factor (e.g. set to 2.0 if Z is sampled half as dense as X or Y)

• net_avg (bool (optional, default True)) – runs the 4 built-in networks and averages them if True, runs one network if False

• augment (bool (optional, default False)) – tiles image with overlapping tiles and flips overlapped regions to augment

• tile (bool (optional, default True)) – tiles image to ensure GPU/CPU memory usage limited (recommended)

• tile_overlap (float (optional, default 0.1)) – fraction of overlap of tiles when computing flows

• resample (bool (optional, default True)) – run dynamics at original image size (will be slower but create more accurate boundaries)

• interp (bool (optional, default True)) – interpolate during 2D dynamics (not available in 3D) (in previous versions it was False)

• flow_threshold (float (optional, default 0.4)) – flow error threshold (all cells with errors below threshold are kept) (not used for 3D)

• mask_threshold (float (optional, default 0.0)) – all pixels with value above threshold kept for masks, decrease to find more and larger masks

• dist_threshold (float (optional, default None) DEPRECATED) – use mask_threshold instead

• cellprob_threshold (float (optional, default None) DEPRECATED) – use mask_threshold instead

• min_size (int (optional, default 15)) – minimum number of pixels per mask, can turn off with -1

• stitch_threshold (float (optional, default 0.0)) – if stitch_threshold>0.0 and not do_3D and equal image sizes, masks are stitched in 3D to return volume segmentation

• rescale (float (optional, default None)) – if diameter is set to None, and rescale is not None, then rescale is used instead of diameter for resizing image

• progress (pyqt progress bar (optional, default None)) – to return progress bar status to GUI

• omni (bool (optional, default False)) – use omnipose mask recontruction features

• calc_trace (bool (optional, default False)) – calculate pixel traces and return as part of the flow

• verbose (bool (optional, default False)) – turn on additional output to logs for debugging

• verbose – turn on additional output to logs for debugging

• transparency (bool (optional, default False)) – modulate flow opacity by magnitude instead of brightness (can use flows on any color background)

• model_loaded (bool (optional, default False)) – internal variable for determining if model has been loaded, used in __main__.py

Returns

• masks (list of 2D arrays, or single 3D array (if do_3D=True)) – labelled image, where 0=no masks; 1,2,…=mask labels

• flows (list of lists 2D arrays, or list of 3D arrays (if do_3D=True)) – flows[k][0] = XY flow in HSV 0-255 flows[k][1] = flows at each pixel flows[k][2] = scalar cell probability (Cellpose) or distance transform (Omnipose) flows[k][3] = final pixel locations after Euler integration flows[k][4] = boundary output (nonempty for Omnipose) flows[k][5] = pixel traces (nonempty for calc_trace=True)

• styles (list of 1D arrays of length 256, or single 1D array (if do_3D=True)) – style vector summarizing each image, also used to estimate size of objects in image

• diams (list of diameters, or float (if do_3D=True))

CellposeModel

class cellpose.models.CellposeModel(gpu=False, pretrained_model=False, model_type=None, net_avg=True, torch=True, diam_mean=30.0, device=None, residual_on=True, style_on=True, concatenation=False, nchan=2, nclasses=3, omni=False)

Parameters

• gpu (bool (optional, default False)) – whether or not to save model to GPU, will check if GPU available

• pretrained_model (str or list of strings (optional, default False)) – path to pretrained cellpose model(s), if None or False, no model loaded

• model_type (str (optional, default None)) – ‘cyto’=cytoplasm model; ‘nuclei’=nucleus model; if None, pretrained_model used

• net_avg (bool (optional, default True)) – loads the 4 built-in networks and averages them if True, loads one network if False

• torch (bool (optional, default True)) – use torch nn rather than mxnet

• diam_mean (float (optional, default 27.)) – mean ‘diameter’, 27. is built in value for ‘cyto’ model

• device (mxnet device (optional, default None)) – where model is saved (mx.gpu() or mx.cpu()), overrides gpu input, recommended if you want to use a specific GPU (e.g. mx.gpu(4))

• model_dir (str (optional, default None)) – overwrite the built in model directory where cellpose looks for models

• omni (use omnipose model (optional, default False)) –

eval(x, batch_size=8, channels=None, channel_axis=None, z_axis=None, normalize=True, invert=False, rescale=None, diameter=None, do_3D=False, anisotropy=None, net_avg=True, augment=False, tile=True, tile_overlap=0.1, resample=True, interp=True, cluster=False, flow_threshold=0.4, mask_threshold=0.0, diam_threshold=12.0, cellprob_threshold=None, dist_threshold=None, compute_masks=True, min_size=15, stitch_threshold=0.0, progress=None, omni=False, calc_trace=False, verbose=False, transparency=False, loop_run=False, model_loaded=False)

segment list of images x, or 4D array - Z x nchan x Y x X

Parameters

• x (list or array of images) – can be list of 2D/3D/4D images, or array of 2D/3D/4D images

• batch_size (int (optional, default 8)) – number of 224x224 patches to run simultaneously on the GPU (can make smaller or bigger depending on GPU memory usage)

• channels (list (optional, default None)) – list of channels, either of length 2 or of length number of images by 2. First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). For instance, to segment grayscale images, input [0,0]. To segment images with cells in green and nuclei in blue, input [2,3]. To segment one grayscale image and one image with cells in green and nuclei in blue, input [[0,0], [2,3]].

• channel_axis (int (optional, default None)) – if None, channels dimension is attempted to be automatically determined

• z_axis (int (optional, default None)) – if None, z dimension is attempted to be automatically determined

• normalize (bool (default, True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

• invert (bool (optional, default False)) – invert image pixel intensity before running network

• rescale (float (optional, default None)) – resize factor for each image, if None, set to 1.0

• diameter (float (optional, default None)) – diameter for each image (only used if rescale is None), if diameter is None, set to diam_mean

• do_3D (bool (optional, default False)) – set to True to run 3D segmentation on 4D image input

• anisotropy (float (optional, default None)) – for 3D segmentation, optional rescaling factor (e.g. set to 2.0 if Z is sampled half as dense as X or Y)

• net_avg (bool (optional, default True)) – runs the 4 built-in networks and averages them if True, runs one network if False

• augment (bool (optional, default False)) – tiles image with overlapping tiles and flips overlapped regions to augment

• tile (bool (optional, default True)) – tiles image to ensure GPU/CPU memory usage limited (recommended)

• tile_overlap (float (optional, default 0.1)) – fraction of overlap of tiles when computing flows

• resample (bool (optional, default True)) – run dynamics at original image size (will be slower but create more accurate boundaries)

• interp (bool (optional, default True)) – interpolate during 2D dynamics (not available in 3D) (in previous versions it was False)

• flow_threshold (float (optional, default 0.4)) – flow error threshold (all cells with errors below threshold are kept) (not used for 3D)

• mask_threshold (float (optional, default 0.0)) – all pixels with value above threshold kept for masks, decrease to find more and larger masks

• dist_threshold (float (optional, default None) DEPRECATED) – use mask_threshold instead

• cellprob_threshold (float (optional, default None) DEPRECATED) – use mask_threshold instead

• compute_masks (bool (optional, default True)) – Whether or not to compute dynamics and return masks. This is set to False when retrieving the styles for the size model.

• min_size (int (optional, default 15)) – minimum number of pixels per mask, can turn off with -1

• stitch_threshold (float (optional, default 0.0)) – if stitch_threshold>0.0 and not do_3D, masks are stitched in 3D to return volume segmentation

• progress (pyqt progress bar (optional, default None)) – to return progress bar status to GUI

• omni (bool (optional, default False)) – use omnipose mask recontruction features

• calc_trace (bool (optional, default False)) – calculate pixel traces and return as part of the flow

• verbose (bool (optional, default False)) – turn on additional output to logs for debugging

• transparency (bool (optional, default False)) – modulate flow opacity by magnitude instead of brightness (can use flows on any color background)

• loop_run (bool (optional, default False)) – internal variable for determining if model has been loaded, stops model loading in loop over images

• model_loaded (bool (optional, default False)) – internal variable for determining if model has been loaded, used in __main__.py

Returns

• masks (list of 2D arrays, or single 3D array (if do_3D=True)) – labelled image, where 0=no masks; 1,2,…=mask labels

• flows (list of lists 2D arrays, or list of 3D arrays (if do_3D=True)) – flows[k][0] = XY flow in HSV 0-255 flows[k][1] = flows at each pixel flows[k][2] = scalar cell probability (Cellpose) or distance transform (Omnipose) flows[k][3] = boundary output (nonempty for Omnipose) flows[k][4] = final pixel locations after Euler integration flows[k][5] = pixel traces (nonempty for calc_trace=True)

• styles (list of 1D arrays of length 64, or single 1D array (if do_3D=True)) – style vector summarizing each image, also used to estimate size of objects in image

loss_fn(lbl, y)

loss function between true labels lbl and prediction y

train(train_data, train_labels, train_files=None, test_data=None, test_labels=None, test_files=None, channels=None, normalize=True, save_path=None, save_every=100, save_each=False, learning_rate=0.2, n_epochs=500, momentum=0.9, SGD=True, weight_decay=1e-05, batch_size=8, nimg_per_epoch=None, rescale=True, min_train_masks=5, omni=False, netstr=None)

train network with images train_data

Parameters

• train_data (list of arrays (2D or 3D)) – images for training

• train_labels (list of arrays (2D or 3D)) – labels for train_data, where 0=no masks; 1,2,…=mask labels can include flows as additional images

• train_files (list of strings) – file names for images in train_data (to save flows for future runs)

• test_data (list of arrays (2D or 3D)) – images for testing

• test_labels (list of arrays (2D or 3D)) – labels for test_data, where 0=no masks; 1,2,…=mask labels; can include flows as additional images

• test_files (list of strings) – file names for images in test_data (to save flows for future runs)

• channels (list of ints (default, None)) – channels to use for training

• normalize (bool (default, True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

• save_path (string (default, None)) – where to save trained model, if None it is not saved

• save_every (int (default, 100)) – save network every [save_every] epochs

• learning_rate (float or list/np.ndarray (default, 0.2)) – learning rate for training, if list, must be same length as n_epochs

• n_epochs (int (default, 500)) – how many times to go through whole training set during training

• weight_decay (float (default, 0.00001)) –

• SGD (bool (default, True)) – use SGD as optimization instead of RAdam

• batch_size (int (optional, default 8)) – number of 224x224 patches to run simultaneously on the GPU (can make smaller or bigger depending on GPU memory usage)

• nimg_per_epoch (int (optional, default None)) – minimum number of images to train on per epoch, with a small training set (< 8 images) it may help to set to 8

• rescale (bool (default, True)) – whether or not to rescale images to diam_mean during training, if True it assumes you will fit a size model after training or resize your images accordingly, if False it will try to train the model to be scale-invariant (works worse)

• min_train_masks (int (default, 5)) – minimum number of masks an image must have to use in training set

• netstr (str (default, None)) – name of network, otherwise saved with name as params + training start time

SizeModel

class cellpose.models.SizeModel(cp_model, device=None, pretrained_size=None, **kwargs)

linear regression model for determining the size of objects in image used to rescale before input to cp_model uses styles from cp_model

Parameters

• cp_model (UnetModel or CellposeModel) – model from which to get styles

• device (mxnet device (optional, default mx.cpu())) – where cellpose model is saved (mx.gpu() or mx.cpu())

• pretrained_size (str) – path to pretrained size model

• omni (bool) – whether or not to use distance-based size metrics corresponding to ‘omni’ model

eval(x, channels=None, channel_axis=None, normalize=True, invert=False, augment=False, tile=True, batch_size=8, progress=None, interp=True, omni=False)

use images x to produce style or use style input to predict size of objects in image

Object size estimation is done in two steps: 1. use a linear regression model to predict size from style in image 2. resize image to predicted size and run CellposeModel to get output masks.

Take the median object size of the predicted masks as the final predicted size.

Parameters

• x (list or array of images) – can be list of 2D/3D images, or array of 2D/3D images

• channels (list (optional, default None)) – list of channels, either of length 2 or of length number of images by 2. First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). For instance, to segment grayscale images, input [0,0]. To segment images with cells in green and nuclei in blue, input [2,3]. To segment one grayscale image and one image with cells in green and nuclei in blue, input [[0,0], [2,3]].

• channel_axis (int (optional, default None)) – if None, channels dimension is attempted to be automatically determined

• normalize (bool (default, True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

• invert (bool (optional, default False)) – invert image pixel intensity before running network

• augment (bool (optional, default False)) – tiles image with overlapping tiles and flips overlapped regions to augment

• tile (bool (optional, default True)) – tiles image to ensure GPU/CPU memory usage limited (recommended)

• progress (pyqt progress bar (optional, default None)) – to return progress bar status to GUI

Returns

• diam (array, float) – final estimated diameters from images x or styles style after running both steps

• diam_style (array, float) – estimated diameters from style alone

train(train_data, train_labels, test_data=None, test_labels=None, channels=None, normalize=True, learning_rate=0.2, n_epochs=10, l2_regularization=1.0, batch_size=8, omni=False)

train size model with images train_data to estimate linear model from styles to diameters

Parameters

• train_data (list of arrays (2D or 3D)) – images for training

• train_labels (list of arrays (2D or 3D)) – labels for train_data, where 0=no masks; 1,2,…=mask labels can include flows as additional images

• channels (list of ints (default, None)) – channels to use for training

• normalize (bool (default, True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

• n_epochs (int (default, 10)) – how many times to go through whole training set (taking random patches) for styles for diameter estimation

• l2_regularization (float (default, 1.0)) – regularize linear model from styles to diameters

• batch_size (int (optional, default 8)) – number of 224x224 patches to run simultaneously on the GPU (can make smaller or bigger depending on GPU memory usage)

Metrics

cellpose.metrics.aggregated_jaccard_index(masks_true, masks_pred)

AJI = intersection of all matched masks / union of all masks

Parameters

• masks_true (list of ND-arrays (int) or ND-array (int)) – where 0=NO masks; 1,2… are mask labels

• masks_pred (list of ND-arrays (int) or ND-array (int)) – ND-array (int) where 0=NO masks; 1,2… are mask labels

Returns

aji

Return type

aggregated jaccard index for each set of masks

cellpose.metrics.average_precision(masks_true, masks_pred, threshold=[0.5, 0.75, 0.9])

average precision estimation: AP = TP / (TP + FP + FN)

This function is based heavily on the fast stardist matching functions (https://github.com/mpicbg-csbd/stardist/blob/master/stardist/matching.py)

Parameters

• masks_true (list of ND-arrays (int) or ND-array (int)) – where 0=NO masks; 1,2… are mask labels

• masks_pred (list of ND-arrays (int) or ND-array (int)) – ND-array (int) where 0=NO masks; 1,2… are mask labels

Returns

• ap (array [len(masks_true) x len(threshold)]) – average precision at thresholds

• tp (array [len(masks_true) x len(threshold)]) – number of true positives at thresholds

• fp (array [len(masks_true) x len(threshold)]) – number of false positives at thresholds

• fn (array [len(masks_true) x len(threshold)]) – number of false negatives at thresholds

cellpose.metrics.boundary_scores(masks_true, masks_pred, scales)

boundary precision / recall / Fscore

cellpose.metrics.flow_error(maski, dP_net, use_gpu=False, device=None)

error in flows from predicted masks vs flows predicted by network run on image

This function serves to benchmark the quality of masks, it works as follows 1. The predicted masks are used to create a flow diagram 2. The mask-flows are compared to the flows that the network predicted

If there is a discrepancy between the flows, it suggests that the mask is incorrect. Masks with flow_errors greater than 0.4 are discarded by default. Setting can be changed in Cellpose.eval or CellposeModel.eval.

Parameters

• maski (ND-array (int)) – masks produced from running dynamics on dP_net, where 0=NO masks; 1,2… are mask labels

• dP_net (ND-array (float)) – ND flows where dP_net.shape[1:] = maski.shape

Returns

• flow_errors (float array with length maski.max()) – mean squared error between predicted flows and flows from masks

• dP_masks (ND-array (float)) – ND flows produced from the predicted masks

cellpose.metrics.mask_ious(masks_true, masks_pred)

return best-matched masks

Flows to masks

cellpose.dynamics.compute_masks(dP, cellprob, bd=None, p=None, inds=None, niter=200, mask_threshold=0.0, diam_threshold=12.0, flow_threshold=0.4, interp=True, do_3D=False, min_size=15, resize=None, verbose=False, use_gpu=False, device=None, nclasses=3)

compute masks using dynamics from dP, cellprob, and boundary

cellpose.dynamics.follow_flows(dP, mask=None, inds=None, niter=200, interp=True, use_gpu=True, device=None, omni=False, calc_trace=False)

define pixels and run dynamics to recover masks in 2D

Pixels are meshgrid. Only pixels with non-zero cell-probability are used (as defined by inds)

Parameters

• dP (float32, 3D or 4D array) – flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx]

• mask ((optional, default None)) – pixel mask to seed masks. Useful when flows have low magnitudes.

• niter (int (optional, default 200)) – number of iterations of dynamics to run

• interp (bool (optional, default True)) – interpolate during 2D dynamics (not available in 3D) (in previous versions + paper it was False)

• use_gpu (bool (optional, default False)) – use GPU to run interpolated dynamics (faster than CPU)

Returns

p – final locations of each pixel after dynamics

Return type

float32, 3D array

cellpose.dynamics.get_masks(p, iscell=None, rpad=20, flows=None, threshold=0.4, use_gpu=False, device=None)

create masks using pixel convergence after running dynamics

Makes a histogram of final pixel locations p, initializes masks at peaks of histogram and extends the masks from the peaks so that they include all pixels with more than 2 final pixels p. Discards masks with flow errors greater than the threshold. :param p: final locations of each pixel after dynamics,

size [axis x Ly x Lx] or [axis x Lz x Ly x Lx].

Parameters

• iscell (bool, 2D or 3D array) – if iscell is not None, set pixels that are iscell False to stay in their original location.

• rpad (int (optional, default 20)) – histogram edge padding

• threshold (float (optional, default 0.4)) – masks with flow error greater than threshold are discarded (if flows is not None)

• flows (float, 3D or 4D array (optional, default None)) – flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx]. If flows is not None, then masks with inconsistent flows are removed using remove_bad_flow_masks.

Returns

M0 – masks with inconsistent flow masks removed, 0=NO masks; 1,2,…=mask labels, size [Ly x Lx] or [Lz x Ly x Lx]

Return type

int, 2D or 3D array

cellpose.dynamics.labels_to_flows(labels, files=None, use_gpu=False, device=None, redo_flows=False)

convert labels (list of masks or flows) to flows for training model

if files is not None, flows are saved to files to be reused

Parameters

labels (list of ND-arrays) – labels[k] can be 2D or 3D, if [3 x Ly x Lx] then it is assumed that flows were precomputed. Otherwise labels[k][0] or labels[k] (if 2D) is used to create flows and cell probabilities.

Returns

flows – flows[k][0] is labels[k], flows[k][1] is cell distance transform, flows[k][2] is Y flow, flows[k][3] is X flow, and flows[k][4] is heat distribution

Return type

list of [4 x Ly x Lx] arrays

cellpose.dynamics.map_coordinates(I, yc, xc, Y)

bilinear interpolation of image ‘I’ in-place with ycoordinates yc and xcoordinates xc to Y

Parameters

• I (C x Ly x Lx) –

• yc (ni) – new y coordinates

• xc (ni) – new x coordinates

• Y (C x ni) – I sampled at (yc,xc)

cellpose.dynamics.masks_to_flows(masks, use_gpu=False, device=None)

convert masks to flows using diffusion from center pixel

Center of masks where diffusion starts is defined to be the closest pixel to the median of all pixels that is inside the mask. Result of diffusion is converted into flows by computing the gradients of the diffusion density map.

Parameters

masks (int, 2D or 3D array) – labelled masks 0=NO masks; 1,2,…=mask labels

Returns

• mu (float, 3D or 4D array) – flows in Y = mu[-2], flows in X = mu[-1]. if masks are 3D, flows in Z = mu[0].

• mu_c (float, 2D or 3D array) – for each pixel, the distance to the center of the mask in which it resides

cellpose.dynamics.masks_to_flows_cpu(masks, device=None)

convert masks to flows using diffusion from center pixel Center of masks where diffusion starts is defined to be the closest pixel to the median of all pixels that is inside the mask. Result of diffusion is converted into flows by computing the gradients of the diffusion density map. :param masks: labelled masks 0=NO masks; 1,2,…=mask labels :type masks: int, 2D array

Returns

• mu (float, 3D array) – flows in Y = mu[-2], flows in X = mu[-1]. if masks are 3D, flows in Z = mu[0].

• mu_c (float, 2D array) – for each pixel, the distance to the center of the mask in which it resides

cellpose.dynamics.masks_to_flows_gpu(masks, device=None)

convert masks to flows using diffusion from center pixel Center of masks where diffusion starts is defined using COM :param masks: labelled masks 0=NO masks; 1,2,…=mask labels :type masks: int, 2D or 3D array

Returns

• mu (float, 3D or 4D array) – flows in Y = mu[-2], flows in X = mu[-1]. if masks are 3D, flows in Z = mu[0].

• mu_c (float, 2D or 3D array) – for each pixel, the distance to the center of the mask in which it resides

cellpose.dynamics.remove_bad_flow_masks(masks, flows, threshold=0.4, use_gpu=False, device=None)

remove masks which have inconsistent flows

Uses metrics.flow_error to compute flows from predicted masks and compare flows to predicted flows from network. Discards masks with flow errors greater than the threshold.

Parameters

• masks (int, 2D or 3D array) – labelled masks, 0=NO masks; 1,2,…=mask labels, size [Ly x Lx] or [Lz x Ly x Lx]

• flows (float, 3D or 4D array) – flows [axis x Ly x Lx] or [axis x Lz x Ly x Lx]

• threshold (float (optional, default 0.4)) – masks with flow error greater than threshold are discarded.

Returns

masks – masks with inconsistent flow masks removed, 0=NO masks; 1,2,…=mask labels, size [Ly x Lx] or [Lz x Ly x Lx]

Return type

int, 2D or 3D array

cellpose.dynamics.step_factor(t)

Euler integration suppression factor.

cellpose.dynamics.steps2D(p, dP, inds, niter, omni=False, calc_trace=False)

run dynamics of pixels to recover masks in 2D

Euler integration of dynamics dP for niter steps

Parameters

• p (float32, 3D array) – pixel locations [axis x Ly x Lx] (start at initial meshgrid)

• dP (float32, 3D array) – flows [axis x Ly x Lx]

• inds (int32, 2D array) – non-zero pixels to run dynamics on [npixels x 2]

• niter (int32) – number of iterations of dynamics to run

Returns

p – final locations of each pixel after dynamics

Return type

float32, 3D array

cellpose.dynamics.steps3D(p, dP, inds, niter)

run dynamics of pixels to recover masks in 3D

Euler integration of dynamics dP for niter steps

Parameters

• p (float32, 4D array) – pixel locations [axis x Lz x Ly x Lx] (start at initial meshgrid)

• dP (float32, 4D array) – flows [axis x Lz x Ly x Lx]

• inds (int32, 2D array) – non-zero pixels to run dynamics on [npixels x 3]

• niter (int32) – number of iterations of dynamics to run

Returns

p – final locations of each pixel after dynamics

Return type

float32, 4D array

Image transforms

cellpose.transforms.average_tiles(y, ysub, xsub, Ly, Lx)

average results of network over tiles

Parameters

• y (float, [ntiles x nclasses x bsize x bsize]) – output of cellpose network for each tile

• ysub (list) – list of arrays with start and end of tiles in Y of length ntiles

• xsub (list) – list of arrays with start and end of tiles in X of length ntiles

• Ly (int) – size of pre-tiled image in Y (may be larger than original image if image size is less than bsize)

• Lx (int) – size of pre-tiled image in X (may be larger than original image if image size is less than bsize)

Returns

yf – network output averaged over tiles

Return type

float32, [nclasses x Ly x Lx]

cellpose.transforms.convert_image(x, channels, channel_axis=None, z_axis=None, do_3D=False, normalize=True, invert=False, nchan=2, omni=False)

return image with z first, channels last and normalized intensities

cellpose.transforms.make_tiles(imgi, bsize=224, augment=False, tile_overlap=0.1)

make tiles of image to run at test-time

if augmented, tiles are flipped and tile_overlap=2.

• original

• flipped vertically

• flipped horizontally

• flipped vertically and horizontally

Parameters

• imgi (float32) – array that’s nchan x Ly x Lx

• bsize (float (optional, default 224)) – size of tiles

• augment (bool (optional, default False)) – flip tiles and set tile_overlap=2.

• tile_overlap (float (optional, default 0.1)) – fraction of overlap of tiles

Returns

• IMG (float32) – array that’s ntiles x nchan x bsize x bsize

• ysub (list) – list of arrays with start and end of tiles in Y of length ntiles

• xsub (list) – list of arrays with start and end of tiles in X of length ntiles

cellpose.transforms.move_axis(img, m_axis=- 1, first=True)

move axis m_axis to first or last position

cellpose.transforms.move_min_dim(img, force=False)

move minimum dimension last as channels if < 10, or force==True

cellpose.transforms.normalize99(Y, lower=0.01, upper=99.99, omni=False)

normalize image so 0.0 is 0.01st percentile and 1.0 is 99.99th percentile

cellpose.transforms.normalize_img(img, axis=- 1, invert=False, omni=False)

normalize each channel of the image so that so that 0.0=1st percentile and 1.0=99th percentile of image intensities

optional inversion

Parameters

• img (ND-array (at least 3 dimensions)) –

• axis (channel axis to loop over for normalization) –

Returns

img – normalized image of same size

Return type

ND-array, float32

cellpose.transforms.original_random_rotate_and_resize(X, Y=None, scale_range=1.0, xy=(224, 224), do_flip=True, rescale=None, unet=False)

augmentation by random rotation and resizing X and Y are lists or arrays of length nimg, with dims channels x Ly x Lx (channels optional) :param X: list of image arrays of size [nchan x Ly x Lx] or [Ly x Lx] :type X: LIST of ND-arrays, float :param Y: list of image labels of size [nlabels x Ly x Lx] or [Ly x Lx]. The 1st channel

of Y is always nearest-neighbor interpolated (assumed to be masks or 0-1 representation). If Y.shape[0]==3 and not unet, then the labels are assumed to be [cell probability, Y flow, X flow]. If unet, second channel is dist_to_bound.

Parameters

• scale_range (float (optional, default 1.0)) – Range of resizing of images for augmentation. Images are resized by (1-scale_range/2) + scale_range * np.random.rand()

• xy (tuple, int (optional, default (224,224))) – size of transformed images to return

• do_flip (bool (optional, default True)) – whether or not to flip images horizontally

• rescale (array, float (optional, default None)) – how much to resize images by before performing augmentations

• unet (bool (optional, default False)) –

Returns

• imgi (ND-array, float) – transformed images in array [nimg x nchan x xy[0] x xy[1]]

• lbl (ND-array, float) – transformed labels in array [nimg x nchan x xy[0] x xy[1]]

• scale (array, float) – amount each image was resized by

cellpose.transforms.pad_image_ND(img0, div=16, extra=1)

pad image for test-time so that its dimensions are a multiple of 16 (2D or 3D)

Parameters

• img0 (ND-array) – image of size [nchan (x Lz) x Ly x Lx]

• div (int (optional, default 16)) –

Returns

• I (ND-array) – padded image

• ysub (array, int) – yrange of pixels in I corresponding to img0

• xsub (array, int) – xrange of pixels in I corresponding to img0

cellpose.transforms.random_rotate_and_resize(X, Y=None, scale_range=1.0, gamma_range=0.5, xy=(224, 224), do_flip=True, rescale=None, unet=False, inds=None, depth=0, omni=False)

augmentation by random rotation and resizing

X and Y are lists or arrays of length nimg, with dims channels x Ly x Lx (channels optional)

Parameters

• X (LIST of ND-arrays, float) – list of image arrays of size [nchan x Ly x Lx] or [Ly x Lx]

• Y (LIST of ND-arrays, float (optional, default None)) – list of image labels of size [nlabels x Ly x Lx] or [Ly x Lx]. The 1st channel of Y is always nearest-neighbor interpolated (assumed to be masks or 0-1 representation). If Y.shape[0]==3 and not unet, then the labels are assumed to be [cell probability, Y flow, X flow]. If unet, second channel is dist_to_bound.

• scale_range (float (optional, default 1.0)) – Range of resizing of images for augmentation. Images are resized by (1-scale_range/2) + scale_range * np.random.rand()

• gamma_range (float (optional, default 0.5)) – Images are gamma-adjusted im**gamma for gamma in (1-gamma_range,1+gamma_range)

• xy (tuple, int (optional, default (224,224))) – size of transformed images to return

• do_flip (bool (optional, default True)) – whether or not to flip images horizontally

• rescale (array, float (optional, default None)) – how much to resize images by before performing augmentations

• unet (bool (optional, default False)) –

Returns

• imgi (ND-array, float) – transformed images in array [nimg x nchan x xy[0] x xy[1]]

• lbl (ND-array, float) – transformed labels in array [nimg x nchan x xy[0] x xy[1]]

• scale (array, float) – amount each image was resized by

cellpose.transforms.reshape(data, channels=[0, 0], chan_first=False)

reshape data using channels

Parameters

• data (numpy array that's (Z x ) Ly x Lx x nchan) – if data.ndim==8 and data.shape[0]<8, assumed to be nchan x Ly x Lx

• channels (list of int of length 2 (optional, default [0,0])) – First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). For instance, to train on grayscale images, input [0,0]. To train on images with cells in green and nuclei in blue, input [2,3].

• invert (bool) – invert intensities

Returns

data

Return type

numpy array that’s (Z x ) Ly x Lx x nchan (if chan_first==False)

cellpose.transforms.reshape_and_normalize_data(train_data, test_data=None, channels=None, normalize=True, omni=False)

inputs converted to correct shapes for training and rescaled so that 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

Parameters

• train_data (list of ND-arrays, float) – list of training images of size [Ly x Lx], [nchan x Ly x Lx], or [Ly x Lx x nchan]

• test_data (list of ND-arrays, float (optional, default None)) – list of testing images of size [Ly x Lx], [nchan x Ly x Lx], or [Ly x Lx x nchan]

• channels (list of int of length 2 (optional, default None)) – First element of list is the channel to segment (0=grayscale, 1=red, 2=green, 3=blue). Second element of list is the optional nuclear channel (0=none, 1=red, 2=green, 3=blue). For instance, to train on grayscale images, input [0,0]. To train on images with cells in green and nuclei in blue, input [2,3].

• normalize (bool (optional, True)) – normalize data so 0.0=1st percentile and 1.0=99th percentile of image intensities in each channel

Returns

• train_data (list of ND-arrays, float) – list of training images of size [2 x Ly x Lx]

• test_data (list of ND-arrays, float (optional, default None)) – list of testing images of size [2 x Ly x Lx]

• run_test (bool) – whether or not test_data was correct size and is useable during training

cellpose.transforms.reshape_train_test(train_data, train_labels, test_data, test_labels, channels, normalize=True, omni=False)

check sizes and reshape train and test data for training

cellpose.transforms.resize_image(img0, Ly=None, Lx=None, rsz=None, interpolation=1, no_channels=False)

resize image for computing flows / unresize for computing dynamics

Parameters

• img0 (ND-array) – image of size [Y x X x nchan] or [Lz x Y x X x nchan] or [Lz x Y x X]

• Ly (int, optional) –

• Lx (int, optional) –

• rsz (float, optional) – resize coefficient(s) for image; if Ly is None then rsz is used

• interpolation (cv2 interp method (optional, default cv2.INTER_LINEAR)) –

Returns

imgs – image of size [Ly x Lx x nchan] or [Lz x Ly x Lx x nchan]

Return type

ND-array

cellpose.transforms.unaugment_tiles(y, unet=False)

reverse test-time augmentations for averaging

Parameters

• y (float32) – array that’s ntiles_y x ntiles_x x chan x Ly x Lx where chan = (dY, dX, cell prob)

• unet (bool (optional, False)) – whether or not unet output or cellpose output

Returns

y

Return type

float32

Plot functions

cellpose.plot.disk(med, r, Ly, Lx)

returns pixels of disk with radius r and center med

cellpose.plot.dx_to_circ(dP, transparency=False, mask=None)

dP is 2 x Y x X => ‘optic’ flow representation

Parameters

• dP (2xLyxLx array) – Flow field components [dy,dx]

• transparency (bool, default False) – magnitude of flow controls opacity, not lightness (clear background)

• mask (2D array) – Multiplies each RGB component to suppress noise

cellpose.plot.image_to_rgb(img0, channels=[0, 0], omni=False)

image is 2 x Ly x Lx or Ly x Lx x 2 - change to RGB Ly x Lx x 3

cellpose.plot.interesting_patch(mask, bsize=130)

get patch of size bsize x bsize with most masks

cellpose.plot.mask_overlay(img, masks, colors=None, omni=False)

overlay masks on image (set image to grayscale)

Parameters

• img (int or float, 2D or 3D array) – img is of size [Ly x Lx (x nchan)]

• masks (int, 2D array) – masks where 0=NO masks; 1,2,…=mask labels

• colors (int, 2D array (optional, default None)) – size [nmasks x 3], each entry is a color in 0-255 range

Returns

RGB – array of masks overlaid on grayscale image

Return type

uint8, 3D array

cellpose.plot.mask_rgb(masks, colors=None)

masks in random rgb colors

Parameters

• masks (int, 2D array) – masks where 0=NO masks; 1,2,…=mask labels

• colors (int, 2D array (optional, default None)) – size [nmasks x 3], each entry is a color in 0-255 range

Returns

RGB – array of masks overlaid on grayscale image

Return type

uint8, 3D array

cellpose.plot.outline_view(img0, maski, color=[1, 0, 0], mode='inner')

Generates a red outline overlay onto image.

cellpose.plot.show_segmentation(fig, img, maski, flowi, channels=[0, 0], file_name=None, omni=False, seg_norm=False, bg_color=None)

plot segmentation results (like on website)

Can save each panel of figure with file_name option. Use channels option if img input is not an RGB image with 3 channels.

Parameters

• fig (matplotlib.pyplot.figure) – figure in which to make plot

• img (2D or 3D array) – image input into cellpose

• maski (int, 2D array) – for image k, masks[k] output from Cellpose.eval, where 0=NO masks; 1,2,…=mask labels

• flowi (int, 2D array) – for image k, flows[k][0] output from Cellpose.eval (RGB of flows)

• channels (list of int (optional, default [0,0])) – channels used to run Cellpose, no need to use if image is RGB

• file_name (str (optional, default None)) – file name of image, if file_name is not None, figure panels are saved

• omni (bool (optional, default False)) – use omni version of normalize99, image_to_rgb

• seg_norm (bool (optional, default False)) – improve cell visibility under labels

• bg_color (float (Optional, default none)) – background color to draw behind flow (visible if flow transparency is on)