Recent Papers

Biomedical Image Segmentation is one of the most critical fields in medial image analysis since it forms a fundamental step for the identification, analysis, and interpretation of several anatomical structures abnormalities within medical images. This review discusses evolution, methodologies, and recent advances in biomedical image segmentation with a focus on deep learning techniques that have transformed this field. Traditional approaches include thresholding, region- based, and edge-based methods, which have laid down the base but proved to be dull as they are not capable of dealing with complex medical images due to their variability in shape, size, and texture. Convolutional neutral networks with the architectures like U-Net, DeepLab, or Mask R-CNN are currently changing paradigms through unprecedented accuracy and robustness towards the segmentation of organs, tumors, or lessons of various imaging modalities including MRI, CT, or even ultrasound. In this manuscript, further developments using the transformer models, hybrid frameworks, and GANs aim to push forward segmentation limits. The review also discusses issues, such as data scarcity, annotation costs, and variability in imaging protocols, and how these can be addressed using transfer learning, data augmentation, and unsupervised learning. This review will gather the current advancements and identify some of the ongoing challenges to inform future directions for biomedical image segmentation, highlighting the requirement for the standardized datasets and clinical validation to make it popular in healthcare.

Copyright © 2025 Aryan Kumar. This is an open access article distributed under the Creative Commons Attribution License.