Digital Pathology

Gland segmentation in prostate histopathological images

[+] Author Affiliations
Malay Singh

National University of Singapore, School of Computing, Department of Computer Science, Singapore

Bioinformatics Institute, Imaging Informatics Division, Matrix, Singapore

Emarene Mationg Kalaw

Bioinformatics Institute, Imaging Informatics Division, Matrix, Singapore

Danilo Medina Giron

Tan Tock Seng Hospital, Department of Pathology, Novena, Singapore

Kian-Tai Chong

Tan Tock Seng Hospital, Department of Urology, Novena, Singapore

Chew Lim Tan

National University of Singapore, School of Computing, Department of Computer Science, Singapore

Hwee Kuan Lee

National University of Singapore, School of Computing, Department of Computer Science, Singapore

Bioinformatics Institute, Imaging Informatics Division, Matrix, Singapore

Institute for Infocomm Research, Image and Pervasive Access Lab, Connexis, Singapore

J. Med. Imag. 4(2), 027501 (Jun 21, 2017). doi:10.1117/1.JMI.4.2.027501
History: Received February 9, 2017; Accepted June 1, 2017
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Abstract.  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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Malay Singh ; Emarene Mationg Kalaw ; Danilo Medina Giron ; Kian-Tai Chong ; Chew Lim Tan, et al.
"Gland segmentation in prostate histopathological images", J. Med. Imag. 4(2), 027501 (Jun 21, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.027501


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