Our understanding of organs at risk is progressing to include physical small tissues such as coronary arteries and the radiosensitivities of many small organs and tissues are high. Therefore, the accurate segmentation of small volumes in external radiotherapy is crucial to protect them from over-irradiation. Moreover, with the development of the particle therapy and on-board imaging, the treatment becomes more accurate and precise. The purpose of this work is to optimize organ segmentation algorithms for small organs. We used 50 three-dimensional (3-D) computed tomography (CT) head and neck images from StructSeg2019 challenge to develop a general-purpose V-Net model to segment 20 organs in the head and neck region. We applied specific strategies to improve the segmentation accuracy of the small volumes in this anatomical region, i.e., the lens of the eye. Then, we used 17 additional head images from OSF healthcare to validate the robustness of the V Net model optimized for small-volume segmentation. With the study of the StructSeg2019 images, we found that the optimization of the image normalization range and classification threshold yielded a segmentation improvement of the lens of the eye of approximately 50%, compared to the use of the V-Net not optimized for small volumes. We used the optimized model to segment 17 images acquired using heterogeneous protocols. We obtained comparable Dice coefficient values for the clinical and StructSeg2019 images (0.61 plus/minus 0.07 and 0.58 plus/minus 0.10 for the left and right lens of the eye, respectively)

翻译：我们对危及器官的理解正逐步扩展到包含冠状动脉等微小组织结构，而许多小器官与组织的放射敏感性较高。因此，在体外放射治疗中精准分割小体积器官对于避免过度照射至关重要。此外，随着粒子治疗与在线成像技术的发展，治疗精度与准确性得到进一步提升。本研究旨在优化针对小器官的器官分割算法。我们采用StructSeg2019挑战赛中的50组三维头颈部计算机断层扫描（CT）图像，构建通用型V-Net模型以分割头颈部20个器官。针对该解剖区域中小体积结构（如晶状体）的分割精度，我们实施了特定优化策略。随后，采用OSF医疗系统中的17组额外头颈部图像验证经小体积分割优化的V-Net模型的鲁棒性。通过对StructSeg2019图像的研究发现，相较于未经小体积优化的V-Net模型，通过优化图像归一化范围与分类阈值可使眼部晶状体分割精度提升约50%。我们使用优化模型对采用异质性采集协议获取的17组图像进行分割，临床图像与StructSeg2019图像获得的Dice系数值相当（左眼晶状体：0.61±0.07；右眼晶状体：0.58±0.10）。