Histopathological Whole Slide Image Analysis Using Context-Based CBIR

• Published in: IEEE transactions on medical imaging Vol. 37; no. 7; pp. 1641 - 1652
• Main Authors: Zheng, Yushan, Jiang, Zhiguo, Zhang, Haopeng, Xie, Fengying, Ma, Yibing, Shi, Huaqiang, Zhao, Yu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Breast - diagnostic imaging; Breast cancer; Breast Neoplasms - diagnostic imaging; Cancer; CBIR; contextual information; Databases, Factual; Diagnosis; Feature extraction; Female; Health risks; Histocytochemistry - methods; Humans; Image analysis; Image classification; Image Interpretation, Computer-Assisted - methods; Image management; Image processing; Image retrieval; Lesions; Malignancy; Medical imaging; Neural Networks (Computer); Pathology; Reproducibility of Results; Tumors; Whole slide image analysis
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2018.2796130

Histopathological image classification (HIC) and content-based histopathological image retrieval (CBHIR) are two promising applications for the histopathological whole slide image (WSI) analysis. HIC can efficiently predict the type of lesion involved in a histopathological image. In general, HIC can aid pathologists in locating high-risk cancer regions from a WSI by providing a cancerous probability map for the WSI. In contrast, CBHIR was developed to allow searches for regions with similar content for a region of interest (ROI) from a database consisting of historical cases. Sets of cases with similar content are accessible to pathologists, which can provide more valuable references for diagnosis. A drawback of the recent CBHIR framework is that a query ROI needs to be manually selected from a WSI. An automatic CBHIR approach for a WSI-wise analysis needs to be developed. In this paper, we propose a novel aided-diagnosis framework of breast cancer using whole slide images, which shares the advantages of both HIC and CBHIR. In our framework, CBHIR is automatically processed throughout the WSI, based on which a probability map regarding the malignancy of breast tumors is calculated. Through the probability map, the malignant regions in WSIs can be easily recognized. Furthermore, the retrieval results corresponding to each sub-region of the WSIs are recorded during the automatic analysis and are available to pathologists during their diagnosis. Our method was validated on fully annotated WSI data sets of breast tumors. The experimental results certify the effectiveness of the proposed method.