Pretrained neural network models for biological segmentation can provide good out-of-the-
box results for many image types. However, such models do not allow users to adapt the …

Generalist models for cellular segmentation, like Cellpose, provide good out-of-the-box
results for many types of images. However, such models do not allow users to adapt the …

Many biological applications require the segmentation of cell bodies, membranes and nuclei
from microscopy images. Deep learning has enabled great progress on this problem, but …

A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …

Towards foundation models of biological image segmentation | Nature Methods Skip to main
content Thank you for visiting nature.com. You are using a browser version with limited support …

Light microscopy combined with well-established protocols of two-dimensional cell culture
facilitates high-throughput quantitative imaging to study biological phenomena. Accurate …

We propose weakly supervised training schemes to train end-to-end cell segmentation
networks that only require a single point annotation per cell as the training label and …

Automated segmentation of cellular electron microscopy (EM) datasets remains a challenge.
Supervised deep learning (DL) methods that rely on region-of-interest (ROI) annotations …

The paper introduces PaintSeg, a new unsupervised method for segmenting objects without
any training. We propose an adversarial masked contrastive painting (AMCP) process …

We address the problem of segmenting cell contours from microscopy images of human
induced pluripotent Retinal Pigment Epithelial stem cells (iRPE) using Convolutional Neural …