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Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks

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机构： [1]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. [2]Department of Electrical and Computer Engineering, University of Tulsa, Tulsa, USA. [3]Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China. [4]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.

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Delineation of the clinical target volume (CTV) and organs-at-risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor-intensive, time-consuming, and subjective. This paper proposes a parallel-path attention fusion network (PPAF-net) to overcome these disadvantages in the delineation task.The PPAF-net utilizes both the texture and structure information of CTV and OARs by employing a U-Net network to capture the high-level texture information, and an up-sampling and down-sampling (USDS) network to capture the low-level structure information to accentuate the boundaries of CTV and OARs. Multi-level features extracted from both networks are then fused together through an attention module to generate the delineation result.The dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB-IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF-net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and etc.) and achieves the state-of-the-art delineation accuracy, respectively, for the CTV and OARs. In terms of the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD), 88.61% and 2.25 cm for the CTV, 92.27% and 0.73 cm for the rectum, 96.74% and 0.68 cm for the bladder, 96.38% and 0.65 cm for the left kidney, 96.79% and 0.63 cm for the right kidney, 93.42% and 0.52 cm for the left femoral head, 93.69% and 0.51 cm for the right femoral head, 87.53% and 1.07 cm for the small intestine, and 91.50% and 0.84 cm for the spinal cord.The proposed automatic delineation network PPAF-net performs well on CTV and OARs segmentation tasks, which has great potential for reducing the burden of radiation oncologists and increasing the accuracy of delineation. In future, radiation oncologists from the West China Hospital of Sichuan University will further evaluate the results of network delineation, making this method helpful in clinical practice.© 2023 American Association of Physicists in Medicine.

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出版当年[2023]版：

大类 | 2 区医学

小类 | 3 区核医学

最新[2023]版：

大类 | 2 区医学

小类 | 3 区核医学

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第一作者机构： [1]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.

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通讯机构： [1]School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. [3]Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University, Chengdu, China. [4]Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China. [*1]Department of Radiation Oncology, Cancer Center, the West China Hospital of Sichuan University,Chengdu 610041, China. [*2]Yangtze Delta Region Institute (Quzhou),University of Electronic Science and Technology of China, Quzhou 324000,

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