Segment Anything in Medical Images

Table of Contents

Title: Segment Anything in Medical Images

Authors: Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, Bo Wang

Published: Apr 24 2023

Link: https://arxiv.org/abs/2304.12306

Summary (Generated by Microsoft Copilot):

Introduction:

  • MedSAM is a foundation model for universal medical image segmentation, developed to address the lack of generalizability in existing methods.

Challenges:

  • Existing models are often task-specific and struggle with generalization across different medical imaging modalities and tasks.

Methods:

  • MedSAM is trained on a large-scale dataset with over 1.5 million image-mask pairs from 10 imaging modalities and 30 cancer types.

Novelties:

  • MedSAM uses a promptable segmentation approach with bounding boxes, enhancing flexibility and adaptability.

Results:

  • MedSAM outperforms state-of-the-art models in both internal and external validation tasks.

Performances:

  • Demonstrates better accuracy and robustness compared to specialist models, achieving high Dice Similarity Coefficient (DSC) scores.

Limitations:

  • Modality imbalance in the training set and difficulty in segmenting vessel-like branching structures.

Discussion:

  • MedSAM shows potential for improving diagnostic tools and personalizing treatment plans, despite some limitations.