Sheng Tao, Mathai Tejas Sudharshan, Shieh Alexander, Summers Ronald M
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, USA.
Departments of Interventional Radiology and Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12927. doi: 10.1117/12.3008823. Epub 2024 Apr 3.
The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task.
骨骼区域是乳腺癌和前列腺癌转移扩散的常见部位之一。CT通常用于测量骨骼中病变的大小。然而，由于病变的大小、形状和外观差异很大，它们可能很难被发现。此类病变的精确定位将有助于可靠地跟踪其随时间的变化（生长、缩小或状态不变）。为此，非常需要一种自动检测骨病变的技术。在这项初步研究中，我们开发了一种通过代理分割任务在CT容积中检测骨病变（溶骨性、成骨性和混合性）的流程。首先，我们使用放射科医生在CT容积的一些二维切片中预先标记的骨病变，并将它们转换为弱三维分割掩码。然后，我们使用这些弱三维注释训练了一个三维全分辨率nnUNet模型，以分割病变从而检测它们。尽管使用的是不完整和部分训练数据，我们的自动方法在CT中检测骨病变的精度为96.7%，召回率为47.3%。据我们所知，我们是第一个尝试通过代理分割任务直接在CT中检测骨病变的。
