Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network
Autor: | Xia Liu, Fuquan Zhang, Zhikai Liu, Bin Xiao, Zheng Miao, Yuliang Sun, Shaobin Wang |
---|---|
Rok vydání: | 2020 |
Předmět: |
Cervical cancer
medicine.medical_specialty business.industry medicine.medical_treatment Biophysics General Physics and Astronomy Rectum Left femoral head General Medicine medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging Radiation therapy 03 medical and health sciences Femoral head 0302 clinical medicine medicine.anatomical_structure 030220 oncology & carcinogenesis Medicine Radiology Nuclear Medicine and imaging Segmentation Radiology business Radiation treatment planning |
Zdroj: | Physica Medica. 69:184-191 |
ISSN: | 1120-1797 |
Popis: | Purpose We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time. Methods We collected 105 patients’ Computed Tomography (CT) scans that diagnosed locally advanced cervical cancer and treated with radiotherapy in one hospital. Seven organs, including the bladder, bone marrow, left femoral head, right femoral head, rectum, small intestine and spinal cord were defined as OARs. The annotated contours of the OARs previously delineated manually by the patient’s radiotherapy oncologist and confirmed by the professional committee consisted of eight experienced oncologists before the radiotherapy were used as the ground truth masks. A multi-class segmentation model based on U-Net was designed to fulfil the OARs segmentation task. The Dice Similarity Coefficient (DSC) and 95th Hausdorff Distance (HD) are used as quantitative evaluation metrics to evaluate the proposed method. Results The mean DSC values of the proposed method are 0.924, 0.854, 0.906, 0.900, 0.791, 0.833 and 0.827 for the bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord, respectively. The mean HD values are 5.098, 1.993, 1.390, 1.435, 5.949, 5.281 and 3.269 for the above OARs respectively. Conclusions Our proposed method can help reduce the inter-observer and intra-observer variability of manual OARs delineation and lessen oncologists’ efforts. The experimental results demonstrate that our model outperforms the benchmark U-Net model and the oncologists’ evaluations show that the segmentation results are highly acceptable to be used in radiation therapy planning. |
Databáze: | OpenAIRE |
Externí odkaz: |