A unified 2D medical image segmentation network (SegmentNet) through distance-awareness and local feature extraction

In addressing the challenges of medical image segmentation, particularly the elusiveness of global context and limitations in leveraging both global and local context simultaneously, we present SegmentNet as a solution. Our approach involves a step-by-step implementation within the reconstructed UNet architecture, tailored to enhance segmentation performance across diverse medical imaging modalities. The first step involves the integration of multi-focus Distance-Aware Mechanisms (DaMs) within skip connections and between successive layers of the encoder in SegmentNet. This strategic placement focuses on extracting unrelated features, ensuring comprehensive consideration of global context. Following this, Local Feature Extractor Blocks (LFEBs) are introduced at the base of the network. Equipped with depthwise separable operations, standard convolutions, smoothed ReLU, and normalization transform, LFEBs target the capture of specific local image features ensuring that features overlooked by DaMs are appropriately considered. These extracted features are then passed on to the decoder portion of SegmentNet, facilitating enhanced prediction of masks thus, optimizing segmentation performance. Evaluated across diverse datasets, including Breast Ultrasound Images (BUSI), Chest X-ray images (CXRI), and Diabetic Retinal Fundus Images (DRFI), SegmentNet excels. The segmentation evaluation results in terms of accuracy, Jaccard, and specificity are respectively recorded for BUSI, CXRI, and DRFI to be (93.88 %, 98.96 %, and 99.17 %), (99.28 %, 99.58 %, and 99.83 %), and (95.77 %, 95.95 %, and 99.94 %). Thus, showing that the incorporation of DaMs and LFEBs in SegmentNet emerges as a robust solution demonstrating precise 2D medical image segmentation across various modalities. This advancement holds significant potential for diverse clinical applications, promising improved patient care.