Automatic Segmentation of Organs-At-Risk and Clinical Target Volume for Cervical Cancer Using Manifold Learning

Automatic segmentation of Organs-At-Risk (OARs) and Clinical Target Volume(CTV) is crucial for the radiotherapy treatment planning of cervical cancer. This task is challenging due to the variation in sizes, shapes, and positions as well as the similar textures among the OARs and CTV. In this paper, we propose a manifold learning-based method based on U-Net. Firstly, the weight matrix of each convolutional layer is constrained to the Stiefel manifold. This constraint enhances the model’s ability to preserve the consistency of the learned feature from CT images. Secondly, we transform the optimization in Euclidean space into Riemannian optimization. This enables the model to optimize the segmentation performance on the manifold space, allowing the model to adapt to the irregular shapes of CTV and OARs. Our experimental results demonstrate that the proposed manifold learning-based method achieves superior performance in segmenting OARs and CTV for cervical cancer as compared to other SOTA methods. Overall, our proposed method demonstrates the potential of manifold learning techniques to improve the segmentation performance of medical images.