Self-supervised dense representation learning for live-cell microscopy with time arrow prediction

1 code implementation • 9 May 2023 • Benjamin Gallusser, Max Stieber, Martin Weigert

Here we present a self-supervised method based on time arrow prediction pre-training that learns dense image representations from raw, unlabeled live-cell microscopy videos.

object-detection Object Detection +2

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation • 11 Mar 2023 • Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

Organelle-specific segmentation, spatial analysis, and visualization of volume electron microscopy datasets

1 code implementation • 7 Mar 2023 • Andreas Müller, Deborah Schmidt, Lucas Rieckert, Michele Solimena, Martin Weigert

In this protocol, we describe a practical and annotation-efficient pipeline for organelle-specific segmentation, spatial analysis, and visualization of large volume electron microscopy datasets using freely available, user-friendly software tools that can be run on a single standard workstation.

Segmentation

Nuclei instance segmentation and classification in histopathology images with StarDist

no code implementations • 3 Mar 2022 • Martin Weigert, Uwe Schmidt

Instance segmentation and classification of nuclei is an important task in computational pathology.

Classification Instance Segmentation +2

Practical sensorless aberration estimation for 3D microscopy with deep learning

1 code implementation • 2 Jun 2020 • Debayan Saha, Uwe Schmidt, Qinrong Zhang, Aurelien Barbotin, Qi Hu, Na Ji, Martin J. Booth, Martin Weigert, Eugene W. Myers

Additionally, we study the predictability of individual aberrations with respect to their data requirements and find that the symmetry of the wavefront plays a crucial role.

An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics

1 code implementation • MIDL 2019 • Sarah Schmell, Falk Zakrzewski, Walter de Back, Martin Weigert, Uwe Schmidt, Torsten Wenke, Silke Zeugner, Robert Mantey, Christian Sperling, Ingo Roeder, Pia Hoenscheid, Daniela Aust, Gustavo Baretton

It mimics the pathological assessment and relies on the detection and localization of interphase nuclei based on instance segmentation networks.

Image Classification Instance Segmentation +3

Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy

2 code implementations • 9 Aug 2019 • Martin Weigert, Uwe Schmidt, Robert Haase, Ko Sugawara, Gene Myers

Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects.

3D Object Detection object-detection

Cell Detection with Star-convex Polygons

3 code implementations • 9 Jun 2018 • Uwe Schmidt, Martin Weigert, Coleman Broaddus, Gene Myers

Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.

Cell Detection Cell Segmentation +2

Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

no code implementations • 5 Apr 2017 • Martin Weigert, Loic Royer, Florian Jug, Gene Myers

We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to.

Super-Resolution