Anatomical Sub Region

Anatomical sub-region identification focuses on precisely delineating specific areas within larger anatomical structures in medical images, primarily to improve the accuracy and efficiency of downstream analyses like segmentation and classification. Current research employs deep learning models, such as U-Net and variations incorporating convolutional neural networks (CNNs) and fully connected networks (FCNs), often leveraging atlas registration techniques for improved speed and robustness. This work is crucial for advancing medical image analysis, enabling more precise diagnoses (e.g., glaucoma detection) and quantitative assessments of disease (e.g., Parkinson's), ultimately leading to improved patient care and more efficient research workflows.