Multiresolution semantic segmentation of biological structures in digital histopathology

Semantic segmentation in high-resolution, histopathology whole slide images (WSIs) is an important fundamental task in various pathology applications. Convolutional neural networks (CNN) are the state-of-the-art approach for image segmentation. A patch-based CNN approach is often employed because of the large size of WSIs; however, segmentation performance is sensitive to the field-of-view and resolution of the input patches, and balancing the trade-offs is challenging when there are drastic size variations in the segmented structures. We propose a multiresolution semantic segmentation approach, which is capable of addressing the threefold trade-off between field-of-view, computational efficiency, and spatial resolution in histopathology WSIs.

We propose a two-stage multiresolution approach for semantic segmentation of histopathology WSIs of mouse lung tissue and human placenta. In the first stage, we use four different CNNs to extract the contextual information from input patches at four different resolutions. In the second stage, we use another CNN to aggregate the extracted information in the first stage and generate the final segmentation masks.

The proposed method reported 95.6%, 92.5%, and 97.1% in our single-class placenta dataset and 97.1%, 87.3%, and 83.3% in our multiclass lung dataset for pixel-wise accuracy, mean Dice similarity coefficient, and mean positive predictive value, respectively.

The proposed multiresolution approach demonstrated high accuracy and consistency in the semantic segmentation of biological structures of different sizes in our single-class placenta and multiclass lung histopathology WSI datasets. Our study can potentially be used in automated analysis of biological structures, facilitating the clinical research in histopathology applications.