背景:锥形束计算机断层扫描(CBCT)图像分割在前列腺癌放射治疗中至关重要,使前列腺的精确描绘准确的治疗计划和交付。然而,CBCT图像质量差给临床实践带来挑战,由于图像噪声等因素,使得注释变得困难,低对比度,和器官变形。
目的:本研究的目的是为无标签目标域(CBCT)创建一个分割模型,利用来自标签丰富的源域(CT)的有价值的见解。通过实现跨模态医学图像分割框架来解决跨不同领域的领域差距,从而实现了这一目标。
方法:我们的方法介绍了一种多尺度域自适应分割方法,同时在图像和特征级别执行域自适应。主要创新在于一种新颖的多尺度解剖正则化方法,其中(i)在多个空间尺度上同时将目标域特征空间与源域特征空间对齐,(ii)跨不同尺度交换信息,从多尺度角度融合知识。
结果:对骨盆CBCT分割任务进行了定量和定性实验。训练数据集包括40个未配对的CBCT-CT图像,其中仅注释了CT图像。验证和测试数据集包括5和10个CT图像,分别,所有的注释。实验结果表明,与其他最先进的跨模态医学图像分割方法相比,我们的方法具有出色的性能。CBCT图像分割结果的Dice相似系数(DSC)为74.6±9.3$74.6\\pm9.3$%,平均对称表面距离(ASSD)为3.9±1.8mm$3.9\\pm1.8\\;\\mathrm{mm}$。统计分析证实了通过我们的方法实现的改进的统计显著性。
结论:与其他方法相比,我们的方法在骨盆CBCT图像分割方面具有优越性。
BACKGROUND: Cone beam computed tomography (CBCT) image segmentation is crucial in prostate cancer radiotherapy, enabling precise delineation of the prostate gland for accurate treatment planning and delivery. However, the poor quality of CBCT images poses challenges in clinical practice, making annotation difficult due to factors such as image noise, low contrast, and organ deformation.
OBJECTIVE: The objective of this study is to create a segmentation model for the label-free target domain (CBCT), leveraging valuable insights derived from the label-rich source domain (CT). This goal is achieved by addressing the domain gap across diverse domains through the implementation of a cross-modality medical image segmentation framework.
METHODS: Our approach introduces a multi-scale domain adaptive segmentation method, performing domain adaptation simultaneously at both the image and feature levels. The primary innovation lies in a novel multi-scale anatomical regularization approach, which (i) aligns the target domain feature space with the source domain feature space at multiple spatial scales simultaneously, and (ii) exchanges information across different scales to fuse knowledge from multi-scale perspectives.
RESULTS: Quantitative and qualitative experiments were conducted on pelvic CBCT segmentation tasks. The training dataset comprises 40 unpaired CBCT-CT images with only CT images annotated. The validation and testing datasets consist of 5 and 10 CT images, respectively, all with annotations. The experimental results demonstrate the superior performance of our method compared to other state-of-the-art cross-modality medical image segmentation methods. The Dice similarity coefficients (DSC) for CBCT image segmentation results is 74.6 ± 9.3 $74.6 \\pm 9.3$ %, and the average symmetric surface distance (ASSD) is 3.9 ± 1.8 mm $3.9\\pm 1.8\\;\\mathrm{mm}$ . Statistical analysis confirms the statistical significance of the improvements achieved by our method.
CONCLUSIONS: Our method exhibits superiority in pelvic CBCT image segmentation compared to its counterparts.