期刊
JOURNAL OF MEDICAL IMAGING
卷 4, 期 2, 页码 -出版社
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JMI.4.2.027501
关键词
AdaBoost; digital pathology; gland segmentation; prostate cancer; support vector machine
资金
- Biomedical Research Council of A*STAR (Agency of Science, Technology and Research), Singapore
- National University of Singapore, Singapore
- Department of Urology at Tan Tock Seng Hospital, Singapore
- Department of Pathology at Tan Tock Seng Hospital, Singapore
Glandular structural features are important for the tumor pathologist in the assessment of cancer malignancy of prostate tissue slides. The varying shapes and sizes of glands combined with the tedious manual observation task can result in inaccurate assessment. There are also discrepancies and low-level agreement among pathologists, especially in cases of Gleason pattern 3 and pattern 4 prostate adenocarcinoma. An automated gland segmentation system can highlight various glandular shapes and structures for further analysis by the pathologist. These objective highlighted patterns can help reduce the assessment variability. We propose an automated gland segmentation system. Forty-three hematoxylin and eosin-stained images were acquired from prostate cancer tissue slides and were manually annotated for gland, lumen, periacinar retraction clefting, and stroma regions. Our automated gland segmentation system was trained using these manual annotations. It identifies these regions using a combination of pixel and object-level classifiers by incorporating local and spatial information for consolidating pixel-level classification results into object-level segmentation. Experimental results show that our method outperforms various texture and gland structure-based gland segmentation algorithms in the literature. Our method has good performance and can be a promising tool to help decrease interobserver variability among pathologists. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).
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