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.
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 (Presented at SPIE Medical Imaging: February 13, 2017; Published: 1 March 2017); https://doi.org/10.1117/12.2254220.
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