A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images

Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency....

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2023-08, Vol.27 (8), p.1-12
Main Authors: Wu, Jia, Yuan, Tingyu, Zeng, Jiachen, Gou, Fangfang
Format: Article
Language:English
Subjects:
Bone cancer
Bone Neoplasms - diagnostic imaging
Bone tumors
Cancer
Cell Nucleus
Data enhancement
Diagnosis
edge enhancement
Hardware
Humans
Image edge detection
Image enhancement
Image processing
Image Processing, Computer-Assisted - methods
Image segmentation
Malignancy
Medical diagnosis
Medical diagnostic imaging
Medical imaging
Medical services
Osteosarcoma
Osteosarcoma - diagnostic imaging
pathological image
Pathology
Sarcoma
Semantic segmentation
Software
stain normalization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency. Existing research on pathological image segmentation often neglects the differences in staining styles and lack of data, without considering medical backgrounds. To alleviate the difficulty in diagnosing osteosarcoma in underdeveloped areas, an intelligent assisted diagnosis and treatment scheme for osteosarcoma pathological images, ENMViT, is proposed. ENMViT utilizes KIN to achieve normalization of mismatched images with limited GPU resources and uses traditional data enhancement methods, such as cleaning, cropping, mosaic, Laplacian sharpening, and other techniques to alleviate the issue of insufficient data. A multi-path semantic segmentation network combining Transformer and CNN is used to segment images, and the degree of edge offset in the spatial domain is introduced into the loss function. Finally, noise is filtered according to the size of the connecting domain. This paper experimented on more than 2000 osteosarcoma pathological images from Central South University. The experimental results demonstrate that this scheme performs well in each stage of the osteosarcoma pathological image processing, and the segmentation results' IoU index is 9.4% higher than the comparative models, demonstrating its significant value in the medical industry.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2023.3278303