Automatic nuclei segmentation of gastrointestinal cancer pathological images based on deformable attention transformer
10.3969/j.issn.1672-8467.2024.03.015
- VernacularTitle:基于可变形注意力transformer的胃肠癌病理图像细胞核自动分割方法
- Author:
Zhi-Xian TANG
1
;
Zhen LI
;
Qiao GUO
;
Jia-Qi HU
;
Xue WANG
;
Xu-Feng YAO
Author Information
1. 上海健康医学院医学影像学院医学影像技术教研室 上海 201318
- Keywords:
deep learning model;
pathological image;
nucleus division;
gastrointestinal cancer;
diagnosis
- From:
Fudan University Journal of Medical Sciences
2024;51(3):396-403
- CountryChina
- Language:Chinese
-
Abstract:
Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.
