Mask-Enhanced Segment Anything Model for Tumor Lesion Semantic
  Segmentation
Hairong Shi Songhao Han Shaofei Huang Yue Liao Guanbin Li Xiangxing Kong Hua Zhu Xiaomu Wang Si Liu
Tumor lesion segmentation on CT or MRI images plays a critical role in cancer
diagnosis and treatment planning. Considering the inherent differences in tumor
lesion segmentation data across various medical imaging modalities and
equipment, integrating medical knowledge into the Segment Anything Model (SAM)
presents promising capability due to its versatility and generalization
potential. Recent studies have attempted to enhance SAM with medical expertise
by pre-training on large-scale medical segmentation datasets. However,
challenges still exist in 3D tumor lesion segmentation owing to tumor
complexity and the imbalance in foreground and background regions. Therefore,
we introduce Mask-Enhanced SAM (M-SAM), an innovative architecture tailored for
3D tumor lesion segmentation. We propose a novel Mask-Enhanced Adapter (MEA)
within M-SAM that enriches the semantic information of medical images with
positional data from coarse segmentation masks, facilitating the generation of
more precise segmentation masks. Furthermore, an iterative refinement scheme is
implemented in M-SAM to refine the segmentation masks progressively, leading to
improved performance. Extensive experiments on seven tumor lesion segmentation
datasets indicate that our M-SAM not only achieves high segmentation accuracy
but also exhibits robust generalization.