H2G-Net: A multi-resolution refinement approach for segmentation of breast cancer region in gigapixel histopathological images - Pedersen, André; Smistad, Erik; Rise, Tor V.; Dale, Vibeke G.; Pettersen, Henrik S.; Nordmo, Tor-Arne S.; Bouget, David; Reinertsen, Ingerid; Valla, Marit (Frontiers in Medicine, 2022)
Over the past decades, histopathological cancer diagnostics has become more complex, and the increasing number of biopsies is a challenge for most pathology laboratories. Thus, development of automatic methods for evaluation of histopathological cancer sections would be of value. In this study, we used 624 whole slide images (WSIs) of breast cancer from a Norwegian cohort. We propose a cascaded convolutional neural network design, called H2G-Net, for segmentation of breast cancer region from gigapixel histopathological images. The design involves a detection stage using a patch-wise method, and a refinement stage using a convolutional autoencoder. To validate the design, we conducted an ablation study to assess the impact of selected components in the pipeline on tumour segmentation. Guiding segmentation, using hierarchical sampling and deep heatmap refinement, proved to be beneficial when segmenting the histopathological images. We found a significant improvement when using a refinement network for post-processing the generated tumour segmentation heatmaps. The overall best design achieved a Dice similarity coefficient of 0.933 ± 0.069 on an independent test set of 90 WSIs. The design outperformed single-resolution approaches, such as cluster-guided, patch-wise high-resolution classification using MobileNetV2 (0.872 ± 0.092) and a low-resolution U-Net (0.874 ± 0.128). In addition, the design performed consistently on WSIs across all histological grades and segmentation on a representative x400 WSI took 58 seconds, using only the central processing unit. The findings demonstrate the potential of utilizing a refinement network to improve patch-wise predictions. The solution is efficient and does not require overlapping patch inference or ensembling. Furthermore, we showed that deep neural networks can be trained using a random sampling scheme that balances on multiple different labels simultaneously, without the need of storing patches on disk.