{"title":"Accurate detection and instance segmentation of unstained living adherent cells in differential interference contrast images","authors":"","doi":"10.1016/j.compbiomed.2024.109151","DOIUrl":null,"url":null,"abstract":"<div><div>Detecting and segmenting unstained living adherent cells in differential interference contrast (DIC) images is crucial in biomedical research, such as cell microinjection, cell tracking, cell activity characterization, and revealing cell phenotypic transition dynamics. We present a robust approach, starting with dataset transformation. We curated 520 pairs of DIC images, containing 12,198 HepG2 cells, with ground truth annotations. The original dataset was randomly split into training, validation, and test sets. Rotations were applied to images in the training set, creating an interim “<span><math><mi>α</mi></math></span> set.” Similar transformations formed “<span><math><mi>β</mi></math></span>” and “<span><math><mi>γ</mi></math></span> sets” for validation and test data. The <span><math><mi>α</mi></math></span> set trained a Mask R-CNN, while the <span><math><mi>β</mi></math></span> set produced predictions, subsequently filtered and categorized. A residual network (ResNet) classifier determined mask retention. The <span><math><mi>γ</mi></math></span> set underwent iterative processing, yielding final segmentation. Our method achieved a weighted average of 0.567 in <span><math><msubsup><mrow><mtext>average precision (AP)</mtext></mrow><mrow><mtext>0.75</mtext></mrow><mrow><mtext>bbox</mtext></mrow></msubsup></math></span> and 0.673 in <span><math><msubsup><mrow><mtext>AP</mtext></mrow><mrow><mtext>0.75</mtext></mrow><mrow><mtext>segm</mtext></mrow></msubsup></math></span>, both outperforming major algorithms for cell detection and segmentation. Visualization also revealed that our method excels in practicality, accurately capturing nearly every cell, a marked improvement over alternatives.</div></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524012368","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Detecting and segmenting unstained living adherent cells in differential interference contrast (DIC) images is crucial in biomedical research, such as cell microinjection, cell tracking, cell activity characterization, and revealing cell phenotypic transition dynamics. We present a robust approach, starting with dataset transformation. We curated 520 pairs of DIC images, containing 12,198 HepG2 cells, with ground truth annotations. The original dataset was randomly split into training, validation, and test sets. Rotations were applied to images in the training set, creating an interim “ set.” Similar transformations formed “” and “ sets” for validation and test data. The set trained a Mask R-CNN, while the set produced predictions, subsequently filtered and categorized. A residual network (ResNet) classifier determined mask retention. The set underwent iterative processing, yielding final segmentation. Our method achieved a weighted average of 0.567 in and 0.673 in , both outperforming major algorithms for cell detection and segmentation. Visualization also revealed that our method excels in practicality, accurately capturing nearly every cell, a marked improvement over alternatives.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.