Uncertainty Driven Pooling Network for Microvessel Segmentation in Routine Histology Images

Lymphovascular invasion (LVI) and tumor angiogenesis are correlated with metastasis, cancer recurrence and poor patient survival. In most of the cases, the LVI quantification and angiogenic analysis is based on microvessel segmentation and density estimation in immunohistochemically (IHC) stained tissues. However, in routine H&E stained images, the microvessels display a high level of heterogeneity in terms of size, shape, morphology and texture which makes microvessel segmentation a non-trivial task. Manual delineation of microvessels for biomarker analysis is labor-intensive, time consuming, irreproducible and can suffer from subjectivity among pathologists. Moreover, it is often beneficial to account for the uncertainty of a prediction when making a diagnosis. To address these challenges, we proposed a framework for microvessel segmentation in H&E stained histology images. The framework extends DeepLabV3+ by using an improved dice coefficient based custom loss function and also incorporating an uncertainty prediction mechanism. The proposed method uses an aligned Xception model, followed by atrous spatial pyramid pooling for feature extraction at multiple scales. This architecture counters the challenge of segmenting blood vessels of varying morphological appearance. To incorporate uncertainty, random transformations are introduced at test time for a superior segmentation result and simultaneous uncertainty map generation, highlighting ambiguous regions. The method is evaluated using 1167 images of size \(512\times 512\) pixels, extracted from 13 WSIs of oral squamous cell carcinoma (OSCC) tissue at 20x magnification. The proposed net-work achieves state-of-the-art performance compared to current semantic segmentation deep neural networks (FCN-8, U-Net, SegNet and DeepLabV3+).