MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM

Homogeneous low-molecular-weight heparins with reversible anticoagulant activity

Low-molecular-weight heparins (LMWHs) are carbohydrate-based anticoagulants clinically used to treat thrombotic disorders, but impurities, structural heterogeneity or functional irreversibility can limit treatment options. We report a series of synthetic LMWHs prepared by cost-effective chemoenzymatic methods. The high activity of one defined synthetic LMWH against human factor Xa (FXa) was reversible in vitro and in vivo using protamine, demonstrating that synthetically accessible constructs can have a critical role in the next generation of LMWHs

Local Sampling and Remote Sensing of Sensor Field Data using Mobile Devices.

Local Sampling and Remote Sensing of Sensor Field Data using Mobile Devices