Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network

Abstract

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.

Auto-segmentations by convolutional neural network in cervical and anorectal cancer with clinical structure sets as the ground truth

“…Previous studies on auto-segmentation, using different systems, have shown moderate to high levels of overlap (as measured with the Dice score). The segmentation of femoral heads is highly concordant in general according to previous literature, with a Dice score range of 90-95% [15,16,[27][28][29]), which is in line with the results in this present study (median: 0.91-0.94). The reason for this high overlap is probably the femoral heads having good contrast with the surrounding tissue and often being regular with a well-defined shape.…”

“…In the case of rectal cancer, a previous study evaluated the auto-segmentation of target volume and OAR, and showed varying segmentation accuracy based on the Dice similarity coefficient ranging from 61.8% (colon) to 93.4% (bladder) [15]. Recently on cervical cancer, Liu et al [16] described a method for OAR (but not CTVN) segmentation using CNNs, with structure sets as ground truth. Their method used a modified version of 2D U-Net, hence not fully utilizing the 3D nature of the data.…”

Automatic contouring system for cervical cancer using convolutional neural networks

“…The number of training, validation, and test CT scans we used to train and evaluate this model is the largest to date among deep learning-based female pelvis auto-contouring studies. 25,26 We successfully acquired this high volume of data by using a semi-automatic data curation method. Also, to the best of our knowledge, we are the first to auto-contour nodal and PAN CTVs in the female pelvic region using deep learning.…”

“…We have developed a CNN‐based auto‐contouring tool for three CTVs and 11 normal structures in cervical cancer CTs that can be used for fully automated radiation treatment planning. The number of training, validation, and test CT scans we used to train and evaluate this model is the largest to date among deep learning‐based female pelvis auto‐contouring studies 25,26 . We successfully acquired this high volume of data by using a semi‐automatic data curation method.…”

Compare the Segmentation Accuracy of Heart Substructure on Contrast Enhanced CT by Deep Convolutional Neural Network with Different Loss Functions

“…OARs delineation is a key step before radiotherapy planning. Precise treatment delivery relies heavily on accurate OARs delineation (38). At present, there have been many researches on automatic heart segmentation, but most of them are carried out on high-quality and well-displayed images such as MRI and CTA (39)(40)(41)(42).This research is carried out on radiotherapy positioning CT, which is the basic image of clinical radiotherapy.…”