School of Information Engineering, Huzhou University, Huzhou, 313000, China.
College of Information Science and Technology, Zhejiang Shuren University, Hangzhou, 310015, China.
Sci Rep. 2024 Aug 21;14(1):19425. doi: 10.1038/s41598-024-70288-8.
This paper introduces the efficient medical-images-aimed segment anything model (EMedSAM), addressing the high computational demands and limited adaptability of using SAM for medical image segmentation tasks. We present a novel, compact image encoder, DD-TinyViT, designed to enhance segmentation efficiency through an innovative parameter tuning method called med-adapter. The lightweight DD-TinyViT encoder is derived from the well-known ViT-H using a decoupled distillation approach.The segmentation and recognition capabilities of EMedSAM for specific structures are improved by med-adapter, which dynamically adjusts the model parameters specifically for medical imaging. We conducted extensive testing on EMedSAM using the public FLARE 2022 dataset and datasets from the First Hospital of Zhejiang University School of Medicine. The results demonstrate that our model outperforms existing state-of-the-art models in both multi-organ and lung segmentation tasks.
本文介绍了高效的医学图像目标分割模型（EMedSAM），该模型针对使用 SAM 进行医学图像分割任务时的高计算需求和有限的适应性问题进行了优化。我们提出了一种新颖的紧凑型图像编码器 DD-TinyViT，通过一种称为 med-adapter 的创新参数调整方法来提高分割效率。该轻量级的 DD-TinyViT 编码器是基于著名的 ViT-H 使用解耦蒸馏方法得到的。med-adapter 改善了 EMedSAM 对特定结构的分割和识别能力，它可以针对医学成像动态调整模型参数。我们使用公共 FLARE 2022 数据集和浙江大学医学院第一附属医院的数据集对 EMedSAM 进行了广泛的测试。结果表明，我们的模型在多器官和肺部分割任务中均优于现有的最先进模型。
