PolyRes-Net: A Polyhierarchical Residual Network for Decoding Anatomical Complexity in Medical Image Segmentation

Abstract

Medical image segmentation entails assigning each pixel in an image to its corresponding class label, a challenging task given the considerable anatomical variations in different cases. The encoder-decoder approach, exemplified by architectures such as U-Net, has emerged as the predominant framework for medical imaging segmentation tasks. In recent years, diverse modifications to the U-Net architecture have been explored, giving rise to distinct models that showcase noteworthy results in comparison to the conventional U-Net design. In this paper, we introduce a novel architectural framework, which we refer to as the Polyhierarchical Residual Network (PolyRes-Net). Each encoder step comprises a Multi-Level Residual Block (MLR-block) designed to extract local and global feature maps. Furthermore, each decoder step is preceded by an attention gate, aiding in the extraction of the most salient features from the preceding layer, while skip connections correspond to the respective encoder steps. Lastly, the multi-scale feature aggregation (MSFA) block consolidates features from various decoder steps. Four benchamar datasets are used for evaluating our model: Krusir-SEG, CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset based on two metrics: the Mean Dice Similarity Coefficient (mDSC) and the Mean Intersection Over Union (mIOU). The results of the proposed PolyRes-Net outperformed the state-of-the-art segmentation methods. Specifically, PolyRes-Net achieves the highest mDSC scores of 91.02%, 91.80%, and 89.25% on CVC ClinicDB, 2018 Data Science Bowl, and ISIC-2018 skin lesion segmentation challenge dataset, respectively. Additionally, the highest mIOU scores are 85.60%, 85.32%, and 82.14% for the same datasets, further underscoring the efficacy of the proposed model.

Keywords

• Medical image segmentation
• medical imaging
• deep learning