Weakly Supervised Cell Instance Segmentation by Propagating from Detection Response

November 29, 2019 · Entered Twilight · 🏛 International Conference on Medical Image Computing and Computer-Assisted Intervention

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Repo contents: .gitignore, LICENSE, README.md, _config.yml, detection, detection_predict.py, detection_train.py, docker, graphcut.m, image, likelymapgen.py, main.py, networks, output, propagate_main.py, propagation, requirement.yml, review.py, run_docker.sh, sample_cell_position.txt, utils, weight

Authors Kazuya Nishimura, Dai Fei Elmer Ker, Ryoma Bise arXiv ID 1911.13077 Category eess.IV: Image & Video Processing Cross-listed cs.CV, q-bio.QM Citations 44 Venue International Conference on Medical Image Computing and Computer-Assisted Intervention Repository https://github.com/naivete5656/WSISPDR ⭐ 43 Last Checked 1 month ago

Abstract

Cell shape analysis is important in biomedical research. Deep learning methods may perform to segment individual cells if they use sufficient training data that the boundary of each cell is annotated. However, it is very time-consuming for preparing such detailed annotation for many cell culture conditions. In this paper, we propose a weakly supervised method that can segment individual cell regions who touch each other with unclear boundaries in dense conditions without the training data for cell regions. We demonstrated the efficacy of our method using several data-set including multiple cell types captured by several types of microscopy. Our method achieved the highest accuracy compared with several conventional methods. In addition, we demonstrated that our method can perform without any annotation by using fluorescence images that cell nuclear were stained as training data. Code is publicly available in "https://github.com/naivete5656/WSISPDR".