Purpose: The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming. We describe a fully automated pipeline for MLI estimation, which is capable of producing results comparable to human raters.
Approach: We use a convolutional neural network based on U-Net architecture to segment the diagnostically relevant tissue segments in the whole slide images (WSI) of the mouse lung tissue. The proposed method extracts multiple field-of-view (FOV) images from the tissue segments and screen the FOV images, rejecting images based on presence of certain biological structures (i.e., blood vessels and bronchi). We used color slicing and region growing for segmentation of different biological structures in each FOV image.
Results: The proposed method was tested on ten WSIs from mice and compared against the scores provided by three human raters. In segmenting the relevant tissue segments, our method obtained a mean accuracy, Dice coefficient, and Hausdorff distance of 98.34%, 98.22%, and 109.68 μm, respectively. Our proposed method yields a mean precision, recall, and F1-score of 93.37%, 83.47%, and 87.87%, respectively, in screening of FOV images. There was substantial agreement found between the proposed method and the manual scores (Fleiss Kappa score of 0.76). The mean difference between the calculated MLI score between the automated method and average rater’s score was 2.33 ± 4.13 (4.25 % ± 5.67 % ).
Conclusion: The proposed pipeline for automated calculation of the MLI score demonstrates high consistency and accuracy with human raters and can be a potential replacement for manual/semi-automated approaches in the field.
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.
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