Automated segmentation and recognition of C. elegans whole-body cells.

Autor:	Li Y; Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China., Lai C; Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China., Wang M; Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China., Wu J; Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China., Li Y; College of Life Sciences, Capital Normal University, Beijing 100048, China., Peng H; SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China., Qu L; Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China.; SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, Anhui 230039, China.; Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230039, China.
Jazyk:	angličtina
Zdroj:	Bioinformatics (Oxford, England) [Bioinformatics] 2024 May 02; Vol. 40 (5).
DOI:	10.1093/bioinformatics/btae324
Abstrakt:	Motivation: Accurate segmentation and recognition of C.elegans cells are critical for various biological studies, including gene expression, cell lineages, and cell fates analysis at single-cell level. However, the highly dense distribution, similar shapes, and inhomogeneous intensity profiles of whole-body cells in 3D fluorescence microscopy images make automatic cell segmentation and recognition a challenging task. Existing methods either rely on additional fiducial markers or only handle a subset of cells. Given the difficulty or expense associated with generating fiducial features in many experimental settings, a marker-free approach capable of reliably segmenting and recognizing C.elegans whole-body cells is highly desirable. Results: We report a new pipeline, called automated segmentation and recognition (ASR) of cells, and applied it to 3D fluorescent microscopy images of L1-stage C.elegans with 558 whole-body cells. A novel displacement vector field based deep learning model is proposed to address the problem of reliable segmentation of highly crowded cells with blurred boundary. We then realize the cell recognition by encoding and exploiting statistical priors on cell positions and structural similarities of neighboring cells. To the best of our knowledge, this is the first method successfully applied to the segmentation and recognition of C.elegans whole-body cells. The ASR-segmentation module achieves an F1-score of 0.8956 on a dataset of 116 C.elegans image stacks with 64 728 cells (accuracy 0.9880, AJI 0.7813). Based on the segmentation results, the ASR recognition module achieved an average accuracy of 0.8879. We also show ASR's applicability to other cell types, e.g. platynereis and rat kidney cells. Availability and Implementation: The code is available at https://github.com/reaneyli/ASR. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu