Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis

1 March 2017 Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis

Naiyun Zhou, Xiaxia Yu, Tianhao Zhao, Si Wen, Fusheng Wang, Wei Zhu, Tahsin Kurc, Allen Tannenbaum, Joel Saltz, Yi Gao

Proceedings Volume 10140, Medical Imaging 2017: Digital Pathology; 101400K (2017) https://doi.org/10.1117/12.2254220
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical, biomedical research, and computer vision fields. Among the multiple observable features spanning multiple scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading. As a result it is also the mostly studied target in image computing. Large amount of research papers have devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei, whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more quantitatively validated study for current and future histopathology image analysis field.

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Citation Download Citation

Naiyun Zhou, Xiaxia Yu, Tianhao Zhao, Si Wen, Fusheng Wang, Wei Zhu, Tahsin Kurc, Allen Tannenbaum, Joel Saltz, and Yi Gao "Evaluation of nucleus segmentation in digital pathology images through large scale image synthesis", Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400K (1 March 2017); https://doi.org/10.1117/12.2254220

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