Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy
| Autor: | S. Shailja, Jiaxiang Jiang, B.S. Manjunath |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0301 basic medicine Computer Science - Machine Learning Source code Computer science Computer Vision and Pattern Recognition (cs.CV) media_common.quotation_subject Pipeline (computing) Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Tracking (particle physics) Machine Learning (cs.LG) 03 medical and health sciences 0202 electrical engineering electronic engineering information engineering Segmentation Cluster analysis media_common business.industry Deep learning Centroid Pattern recognition Image segmentation 030104 developmental biology 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ISBI |
| Popis: | We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image. This is motivated by the observation that current state-of-the-art deep learning methods do not result in accurate boundaries when the training data is weakly annotated. Towards this, a 3D U-Net is trained to get the centroid of the nuclei and integrated with a simple linear iterative clustering (SLIC) supervoxel algorithm that provides better adherence to cluster boundaries. To track these segmented nuclei, our algorithm utilizes the relative nuclei location depicting the processes of nuclei division and apoptosis. The proposed algorithmic pipeline achieves better segmentation performance compared to the state-of-the-art method in Cell Tracking Challenge (CTC) 2019 and comparable performance to state-of-the-art methods in IEEE ISBI CTC2020 while utilizing very few pixel-wise annotated data. Detailed experimental results are provided, and the source code is available on GitHub. To be submitted to ISBI 2021 |
| Databáze: | OpenAIRE |
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