This work presents a valuable self-supervised method for the segmentation of 3D cells in microscopy images, alongside an implementation as a Napari plugin and an annotated dataset. While the Napari ...

FCIS is a fully convolutional end-to-end solution for instance segmentation, which won the first place in COCO segmentation challenge 2016. FCIS is initially described in a CVPR 2017 spotlight paper.

This is the official PyTorch implementation of the paper RetSeg3D: Retention-based 3D Semantic Segmentation for Autonomous Driving, by Gopi Krishna Erabati and Helder Araujo. G. K. Erabati and H.

Various pre-trained deep learning models for the segmentation of bioimages have been made available as developer-to-end-user solutions. They are optimized for ease of use and usually require neither ...