Jie Yuan

Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on CT/MR Dual-Modality Images

Accurate delineation of the clinical target volume (CTV) is a crucial prerequisite for safe and effective radiotherapy characterized. This study addresses the integration of magnetic resonance (MR) images to aid in target delineation on computed tomography (CT) images. However, obtaining MR images directly can be challenging. Therefore, we employ AI-based image generation techniques to "intelligentially generate" MR images from CT images to improve CTV delineation based on CT images. To generate high-quality MR images, we propose an attention-guided single-loop image generation model. The model can yield higher-quality images by introducing an attention mechanism in feature extraction and enhancing the loss function. Based on the generated MR images, we propose a CTV segmentation model fusing multi-scale features through image fusion and a hollow space pyramid module to enhance segmentation accuracy. The image generation model used in this study improves the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) from 14.87 and 0.58 to 16.72 and 0.67, respectively, and improves the feature distribution distance and learning-perception image similarity from 180.86 and 0.28 to 110.98 and 0.22, achieving higher quality image generation. The proposed segmentation method demonstrates high accuracy, compared with the FCN method, the intersection over union ratio and the Dice coefficient are improved from 0.8360 and 0.8998 to 0.9043 and 0.9473, respectively. Hausdorff distance and mean surface distance decreased from 5.5573 mm and 2.3269 mm to 4.7204 mm and 0.9397 mm, respectively, achieving clinically acceptable segmentation accuracy. Our method might reduce physicians' manual workload and accelerate the diagnosis and treatment process while decreasing inter-observer variability in identifying anatomical structures.

Bone marrow sparing oriented multi-model image registration in cervical cancer radiotherapy

Cervical cancer poses a serious threat to the health of women and radiotherapy is one of the primary treatment methods for this condition. However, this treatment is associated with a high risk of causing acute hematologic toxicity. Delineating the bone marrow (BM) for sparing based on computer tomography (CT) images before radiotherapy can effectively avoid this risk. Unfortunately, compared to magnetic resonance (MR) images, CT images lack the ability to express the activity of BM. Therefore, medical practitioners currently manually delineate the BM on CT images by corresponding to MR images. However, the manual delineation of BM is time-consuming and cannot guarantee accuracy due to the inconsistency of the CT-MR multimodal images. This study proposes a multimodal image-oriented automatic registration method for pelvic BM sparing. The proposed method includes three-dimensional (3D) bone point clouds reconstruction and an iterative closest point registration based on a local spherical system for marking BM on CT images. By introducing a joint coordinate system that combines the global Cartesian coordinate system with the local point clouds' spherical coordinate system, the increasement of point descriptive dimension avoids the local optimal registration and improves the registration accuracy. Experiments on the dataset of patients demonstrate that our proposed method can enhance the multimodal image registration accuracy and efficiency for medical practitioners in BM-sparing of cervical cancer radiotherapy. The method proposed in this contribution might also provide a solution to multimodal registration, especially in multimodal sequential images in other clinical applications, such as the diagnosis of cervical cancer and the preservation of normal organs during radiotherapy.