A robust image segmentation and synthesis pipeline for histopathology

Abstract

Significant diagnostic variability between and within observers persists in pathology, despite the fact that digital slide images provide the ability to measure and quantify features much more precisely compared to conventional methods. Automated and accurate segmentation of cancerous cell and tissue regions can streamline the diagnostic process, providing insights into the cancer progression, and helping experts decide on the most effective treatment. Here, we evaluate the performance of the proposed PathoSeg model, with an architecture comprising of a modified HRNet encoder and a UNet++ decoder integrated with a CBAM block to utilize attention mechanism for an improved segmentation capability. We demonstrate that PathoSeg outperforms the current state-of-the-art (SOTA) networks in both quantitative and qualitative assessment of instance and semantic segmentation. Notably, we leverage the use of synthetic data generated by PathopixGAN, which effectively addresses the data imbalance problem commonly encountered in histopathology datasets, further improving the performance of PathoSeg. It utilizes spatially adaptive normalization within a generative and discriminative mechanism to synthesize diverse histopathological environments dictated through semantic information passed through pixel-level annotated Ground Truth semantic masks.Besides, we contribute to the research community by providing an in-house dataset that includes semantically segmented masks for breast carcinoma tubules (BCT), micro/macrovesicular steatosis of the liver (MSL), and prostate carcinoma glands (PCG). In the first part of the dataset, we have a total of 14 whole slide images from 13 patients' liver, with fat cell segmented masks, totaling 951 masks of size 512 × 512 pixels. In the second part, it includes 17 whole slide images from 13 patients with prostate carcinoma gland segmentation masks, amounting to 30,000 masks of size 512 × 512 pixels. In the third part, the dataset contains 51 whole slides from 36 patients, with breast carcinoma tubule masks totaling 30,000 masks of size 512 × 512 pixels. To ensure transparency and encourage further research, we will make this dataset publicly available for non-commercial and academic purposes. To facilitate reproducibility and encourage further research, we will also make our code and pre-trained models publicly available at https://github.com/DeepMIALab/PathoSeg.

Keywords: Deep learning; Instance segmentation; Semantic segmentation; Synthetic data.