Machine learning-based approach for fully automated segmentation of muscularis propria from histopathology images of intestinal specimens

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