Core Concepts
The author introduces the Mask-Enhanced SAM (M-SAM) architecture tailored for 3D tumor lesion segmentation, enhancing segmentation accuracy and generalization capabilities.
Abstract
The content discusses the development of the Mask-Enhanced Segment Anything Model (M-SAM) for 3D tumor lesion segmentation. It addresses challenges in medical imaging, introduces a novel Mask-Enhanced Adapter (MEA), and implements an iterative refinement scheme to improve segmentation masks progressively. Extensive experiments on various datasets demonstrate the effectiveness and robustness of M-SAM in achieving high segmentation accuracy and generalization.
Stats
Recent studies have attempted to enhance SAM with medical expertise by pre-training on large-scale medical segmentation datasets.
Challenges exist in 3D tumor lesion segmentation due to tumor complexity and imbalance in foreground and background regions.
M-SAM introduces a novel Mask-Enhanced Adapter (MEA) to enrich semantic information with positional data from coarse segmentation masks.
An iterative refinement scheme is implemented in M-SAM to progressively refine segmentation masks, leading to improved performance.
M-SAM achieves high segmentation accuracy and exhibits robust generalization across seven tumor lesion segmentation datasets.
Quotes
"We introduce a novel Mask-Enhanced SAM (M-SAM) architecture to explore the application of SAM in the medical domain."
"Our MEA is designed to be plug-and-play, enhancing image embeddings with positional information from coarse masks."
"Through iterative refinement, the segmentation masks can be gradually improved, boosting overall performance."