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      A systematic evaluation of computation methods for cell segmentation

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          Abstract

          Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.

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          Attention Is All You Need

          Ashish Vaswani, Noam Shazeer, Niki Parmar … (2017)
          The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
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            Cellpose: a generalist algorithm for cellular segmentation

            Carsen Stringer, Tim Wang, Michalis Michaelos … (2020)
            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 current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.
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              Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

              Shaoqing Ren, Kaiming He, Ross Girshick … (2015)
              State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. Extended tech report
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                Author and article information

                Journal
                Journal ID (nlm-ta): bioRxiv
                Journal ID (publisher-id): BIORXIV
                Title: bioRxiv
                Publisher: Cold Spring Harbor Laboratory
                Publication date (Electronic): 31 January 2024
                Electronic Location Identifier: 2024.01.28.577670
                Affiliations
                [1 ]Department of Computer Engineering, Rochester Institute of Technology, Rochester, NY, USA.
                [2 ]Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
                [3 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
                [4 ]Department of Biostatistics, Harvard T.H.Chan School of Public Health, Boston, MA, USA.
                [5 ]Department of Computer Science & eScience Institute, University of Washington, Seattle, WA, USA.
                [6 ]Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
                [7 ]Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA.
                Author notes
                [*]

                These authors contributed equally

                AUTHOR CONTRIBUTIONS

                Z.J. conceived the study. Y.W., J.Z., H.X., and C.H. performed the analysis. J.Z., Z.T., D.Zhao, D.Zhou, G.T., D.L., and Z.J. provided computational resource and advised the analysis. Y.W., J.Z., H.X., D.L, and Z.J. wrote the manuscript.

                [† ] Corresponding author. dxleec@ 123456rit.edu and zhicheng.ji@ 123456duke.edu
                Article
                DOI: 10.1101/2024.01.28.577670
                PMC ID: 10862744
                PubMed ID: 38352578
                SO-VID: bcbe1e9a-8542-4ab1-b518-64c39d051852
                License:

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.

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