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18 March 2019 Machine learning-based approach for fully automated segmentation of muscularis propria from histopathology images of intestinal specimens
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Abstract
Hirschsprung’s disease is a motility disorder that requires the assessment of the Auerbach’s (myenteric) plexus located in muscularis propria layer. In this paper, we describe a fully automated method for segmenting muscularis propria (MP) from histopathology images of intestinal specimens using a method based on convolutional neural network (CNN). Such a network has the potential to learn intensity, textural, and shape features from the manual segmented images to accomplish distinction between MP and non-MP tissues from histopathology images. We used a dataset consisted of 15 images and trained our model using approximately 3,400,000 image patches extracted from six images. The trained CNN was employed to determine the boundary of MP on 9 test images (including 75,000,000 image patches). The resultant segmentation maps were compared with the manual segmentations to investigate the performance of our proposed method for MP delineation. Our technique yielded an average Dice similarity coefficient (DSC) and absolute surface difference (ASD) of 92.36 ± 2.91% and 1.78 ± 1.57 mm2 respectively, demonstrating that the proposed CNNbased method is capable of accurately segmenting MP tissue from histopathology images.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Conor McKeen, Fatemeh Zabihollahy, Jinu Kurian, Adrian D. C. Chan, Dina El Demellawy, and Eranga Ukwatta "Machine learning-based approach for fully automated segmentation of muscularis propria from histopathology images of intestinal specimens", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 109560P (18 March 2019); https://doi.org/10.1117/12.2512970
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