As the significance of simulation in medical care and intervention continues
to grow, it is anticipated that a simplified and low-cost platform can be set
up to execute personalized diagnoses and treatments. 3D Slicer can not only
perform medical image analysis and visualization but can also provide surgical
navigation and surgical planning functions. In this paper, we have chosen 3D
Slicer as our base platform and monocular cameras are used as sensors. Then, We
used the neural radiance fields (NeRF) algorithm to complete the 3D model
reconstruction of the human head. We compared the accuracy of the NeRF
algorithm in generating 3D human head scenes and utilized the MarchingCube
algorithm to generate corresponding 3D mesh models. The individual's head pose,
obtained through single-camera vision, is transmitted in real-time to the scene
created within 3D Slicer. The demonstrations presented in this paper include
real-time synchronization of transformations between the human head model in
the 3D Slicer scene and the detected head posture. Additionally, we tested a
scene where a tool, marked with an ArUco Maker tracked by a single camera,
synchronously points to the real-time transformation of the head posture. These
demos indicate that our methodology can provide a feasible real-time simulation
platform for nasopharyngeal swab collection or intubation.

我们使用 3D Slicer 作为基础平台，利用单目相机将人头进行 3D 模型重建，实时通过单目视觉传输头部姿态，并演示了利用 ArUco Marker 追踪工具实时指向头部姿态变化的可行性，为鼻咽拭子采集或插管等提供了一个可行的实时仿真平台。

通过单目摄像机实时进行航行和安全进入自然开口的头部姿态估计和三维神经表面重建

Head Pose Estimation and 3D Neural Surface Reconstruction via Monocular  Camera in situ for Navigation and Safe Insertion into Natural Openings

TomoSAM has been developed to integrate the cutting-edge Segment Anything
Model (SAM) into 3D Slicer, a highly capable software platform used for 3D
image processing and visualization. SAM is a promptable deep learning model
that is able to identify objects and create image masks in a zero-shot manner,
based only on a few user clicks. The synergy between these tools aids in the
segmentation of complex 3D datasets from tomography or other imaging
techniques, which would otherwise require a laborious manual segmentation
process. The source code associated with this article can be found at
this https URL

TomoSAM 是一个将前沿的 Segment Anything Model 集成到 3D Slicer 中的软件，它是一个能够在零样本学习的基础上，基于用户点击来识别物体和创建图像 mask 的深度学习模型，这个工具的协同作用有助于对来自断层扫描或其他成像技术的复杂 3D 数据集进行分割。

TomoSAM：使用 SAM 进行层析成像分割的 3D Slicer 扩展

TomoSAM: a 3D Slicer extension using SAM for tomography segmentation

The Segment Anything Model (SAM) is a new image segmentation tool trained
with the largest segmentation dataset at this time. The model has demonstrated
that it can create high-quality masks for image segmentation with good
promptability and generalizability. However, the performance of the model on
medical images requires further validation. To assist with the development,
assessment, and utilization of SAM on medical images, we introduce Segment Any
Medical Model (SAMM), an extension of SAM on 3D Slicer, a widely-used
open-source image processing and visualization software that has been
extensively used in the medical imaging community. This open-source extension
to 3D Slicer and its demonstrations are posted on GitHub
(this https URL). SAMM achieves 0.6-second latency of a
complete cycle and can infer image masks in nearly real-time.

SAM 模型在图像分割方面表现良好，但在医学图像方面还需要进一步验证。我们介绍了 SAMM，它是 SAM 的扩展，用于在 3D Slicer 上处理医学图像，并能够近乎实时地产生图像掩膜。