Mediastinal image detection and segmentation based on MedSAM

Clinical practice significantly benefits from the precise delineation of medical images, which aids in the precise identification of conditions, the formulation of therapeutic strategies, and the ongoing surveillance of illnesses. However, current approaches to segmentation are predominantly designed for particular image patterns or specific disease categories. Given the infrequent occurrence and the broad spectrum of mediastinal tumor types, achieving an accurate diagnosis solely through visual inspection presents substantial challenges. This design is a deep learning medical image segmentation system based on MedSAM, which has processed a series of custom datasets and trained them based on pre trained models to enable semantic segmentation and detection of lesion areas.