The Segment Anything Model (SAM) has drawn significant attention from researchers who work on medical image segmentation because of its generalizability. However, researchers have found that SAM may have limited performance on medical images compared to state-of-the-art non-foundation models. Regardless, the community sees potential in extending, fine-tuning, modifying, and evaluating SAM for analysis of medical imaging. An increasing number of works have been published focusing on the mentioned four directions, where variants of SAM are proposed. To this end, a unified platform helps push the boundary of the foundation model for medical images, facilitating the use, modification, and validation of SAM and its variants in medical image segmentation. In this work, we introduce SAMM Extended (SAMME), a platform that integrates new SAM variant models, adopts faster communication protocols, accommodates new interactive modes, and allows for fine-tuning of subcomponents of the models. These features can expand the potential of foundation models like SAM, and the results can be translated to applications such as image-guided therapy, mixed reality interaction, robotic navigation, and data augmentation.
Transcranial Magnetic Stimulation (TMS) is a neurostimulation technique based on the principle of electromagnetic induction of an electric field in the brain with both research and clinical applications. To produce an optimal neuro-modulatory effect, TMS coil must be placed on the head and oriented accurately with respect to the region of interest within the brain. A robotic method can enhance the accuracy and facilitate the procedure for TMS coil placement. This work presents two system improvements for robot-assisted TMS (RA-TMS) application. Previous systems have used outside-in tracking method where a stationary external infrared (IR) tracker is used as a reference point to track the head and TMS coil positions. This method is prone to losing track of the coil or the head if the IR camera is blocked by the robotic arm during its motion. To address this issue, we implemented an inside-out tracking method by mounting a portable IR camera on the robot end-effector. This method guarantees that the line of sight of the IR camera is not obscured by the robotic arm at any time during its motion. We also integrated a portable projection mapping device (PPMD) into the RA-TMS system to provide visual guidance during TMS application. PPMD can track the head via an IR tracker, and can project a planned contact point of TMS coil on the head or overlay the underlying brain anatomy in real-time.
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