Automated analysis of paraspinal muscles: segmentation and multi-parameter quantification in lumbar CT using convolutional neural network.

Automatic breast ultrasound (ABUS) imaging provides complementary information when other imaging modalities (i.e., mammography) are not conclusive in the task of tumor detection. It enables sectional plane visualization, simplified temporal comparison and coronal depiction, and features higher reproducibility than conventional ultrasound imaging. Although the 3D ABUS acquisition significantly reduces the acquisition time and cost, the manual segmentation of tumor on 3D ABUS could be time-consuming and labor-intensive due to its high slice number. This work aims to develop a deep-learning-based method to automatically segment the breast tumor on 3D ABUS. We integrated mask scoring-based self-correlation strategy into the R-CNN-based method to force the final segmented tumor contour to be more reasonable. We tested the performance of the proposed method using 3D ABUS of 40 patients who are confirmed with breast tumor through four-fold cross validation test. The comparison between our results and the ground truth contours was quantified by metrics including the Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95). The mean DSC, JAC, and HD95 were 0.85 ± 0.10, 0.75 ± 0.14, and 1.65 ± 1.20 (mm), respectively.

Delineating the clinical target volume (CTV) and organs at risk (OARs) is crucial in rectal cancer radiotherapy. However, the accuracy of manual delineation (MD) is variable and the process is time consuming. Automatic delineation (AD) may be a solution to produce quicker and more accurate contours. In the present study, a convolutional neural network (CNN)-based AD tool was clinically evaluated to analyze its accuracy and efficiency in rectal cancer. CT images were collected from 148 supine patients in whom tumor stage and type of surgery were not differentiated. The rectal cancer contours consisted of CTV and OARs, where the OARs included the bladder, left and right femoral head, left and right kidney, spinal cord and bowel bag. The MD contours reviewed and modified together by a senior radiation oncologist committee were set as the reference values. The Dice similarity coefficient (DSC), Jaccard coefficient (JAC) and Hausdorff distance (HD) were used to evaluate the AD accuracy. The correlation between CT slice number and AD accuracy was analyzed, and the AD accuracy for different contour numbers was compared. The time recorded in the present study included the MD time, AD time for different CT slice and contour numbers and the editing time for AD contours. The Pearson correlation coefficient, paired-sample t-test and unpaired-sample t-test were used for statistical analyses. The results of the present study indicated that the DSC, JAC and HD for CTV using AD were 0.80±0.06, 0.67±0.08 and 6.96±2.45 mm, respectively. Among the OARs, the highest DSC and JAC using AD were found for the right and left kidney, with 0.91±0.06 and 0.93±0.04, and 0.84±0.09 and 0.88±0.07, respectively, and HD was lowest for the spinal cord with 2.26±0.82 mm. The lowest accuracy was found for the bowel bag. The more CT slice numbers, the higher the accuracy of the spinal cord analysis. However, the contour number had no effect on AD accuracy. To obtain qualified contours, the AD time plus editing time was 662.97±195.57 sec, while the MD time was 3294.29±824.70 sec. In conclusion, the results of the present study indicate that AD can significantly improve efficiency and a higher number of CT slices and contours can reduce AD efficiency. The AD tool provides acceptable CTV and OARs for rectal cancer and improves efficiency for delineation.

BackgroundRecognition of the intermuscular spaces within lumbar paraspinal muscles is critically important for using the paramedian muscle-splitting approach to the lumbar spine. As such, it is important to determine the intermuscular spaces within the lumbar paraspinal muscles by utilizing modern medical imaging such as computed tomography (CT) and magnetic resonance imaging (MRI).MethodsA total of 30 adult cadavers were studied by sectional anatomic dissection, and 60 patients were examined using CT (16 slices, 3-mm thickness, 3-mm intersection gap, n = 30) and MRI (3.0T, T2-WI, 5-mm thickness, 1-mm intersection gap, n = 30). The distances between the midline and the superficial points of the intermuscular spaces at different intervertebral disc levels were measured.ResultsBased on study of our cadavers, the mean distances from the midline to the intermuscular space between multifidus and longissimus, from intervertebral disc levels L1–L2 to L5–S1, were 0.9, 1.1, 1.7, 3.0, and 3.5 cm, respectively. Compared with the upper levels (L1–L3), the superficial location at the lower level (L4–S1) is more laterally to the midline (P<0.05). The intermuscular space between sacrospinalis and quadratus lumborum, and that between longissimus and iliocostalis did not exist at L4–S1. The intermuscular spaces in patients also varied at different levels of the lumbar spine showing a low discontinuous density in CT and a high signal in MRI. There were no significant differences between the observations in cadavers and those made using CT and MRI.ConclusionThe intermuscular spaces within the paraspinal muscles vary at different intervertebral disc levels. Preoperative CT and MRI can facilitate selection of the muscle-splitting approach to the lumbar spine. This paper demonstrates the efficacy of medical imaging techniques in surgical planning.

In this study, we present deep learning‐based approaches to automatic segmentation and applicator reconstruction with high accuracy and efficiency in the planning computed tomography (CT) for cervical cancer brachytherapy (BT). A novel three‐dimensional (3D) convolutional neural network (CNN) architecture was proposed and referred to as DSD‐UNET. The dataset of 91 patients received CT‐based BT of cervical cancer was used to train and test DSD‐UNET model for auto‐segmentation of high‐risk clinical target volume (HR‐CTV) and organs at risk (OARs). Automatic applicator reconstruction was achieved with DSD‐UNET‐based segmentation of applicator components followed by 3D skeletonization and polynomial curve fitting. Digitization of the channel paths for tandem and ovoid applicator in the planning CT was evaluated utilizing the data from 32 patients. Dice similarity coefficient (DSC), Jaccard Index (JI), and Hausdorff distance (HD) were used to quantitatively evaluate the accuracy. The segmentation performance of DSD‐UNET was compared with that of 3D U‐Net. Results showed that DSD‐UNET method outperformed 3D U‐Net on segmentations of all the structures. The mean DSC values of DSD‐UNET method were 86.9%, 82.9%, and 82.1% for bladder, HR‐CTV, and rectum, respectively. For the performance of automatic applicator reconstruction, outstanding segmentation accuracy was first achieved for the intrauterine and ovoid tubes (average DSC value of 92.1%, average HD value of 2.3 mm). Finally, HDs between the channel paths determined automatically and manually were 0.88 ± 0.12 mm, 0.95 ± 0.16 mm, and 0.96 ± 0.15 mm for the intrauterine, left ovoid, and right ovoid tubes, respectively. The proposed DSD‐UNET method outperformed the 3D U‐Net and could segment HR‐CTV, bladder, and rectum with relatively good accuracy. Accurate digitization of the channel paths could be achieved with the DSD‐UNET‐based method. The proposed approaches could be useful to improve the efficiency and consistency of treatment planning for cervical cancer BT.

Automatic segmentation of the choroid on optical coherence tomography (OCT) images helps ophthalmologists in diagnosing eye pathologies. Compared to manual segmentations, it is faster and is not affected by human errors. The presence of the large speckle noise in the OCT images limits the automatic segmentation and interpretation of them. To solve this problem, a new curvelet transform-based K-SVD method is proposed in this study. Furthermore, the dataset was manually segmented by a retinal ophthalmologist to draw a comparison with the proposed automatic segmentation technique. In this study, curvelet transform-based K-SVD dictionary learning and Lucy-Richardson algorithm were used to remove the speckle noise from OCT images. The Outer/Inner Choroidal Boundaries (O/ICB) were determined utilizing graph theory. The area between ICB and outer choroidal boundary was considered as the choroidal region. The proposed method was evaluated on our dataset and the average dice similarity coefficient (DSC) was calculated to be 92.14% ± 3.30% between automatic and manual segmented regions. Moreover, by applying the latest presented open-source algorithm by Mazzaferri et al. on our dataset, the mean DSC was calculated to be 55.75% ± 14.54%. A significant similarity was observed between automatic and manual segmentations. Automatic segmentation of the choroidal layer could be also utilized in large-scale quantitative studies of the choroid.

IntroductionNarrow Band Imaging (NBI) is an endoscopic visualization technique useful for upper aero-digestive tract (UADT) cancer detection and margins evaluation. However, NBI analysis is strongly operator-dependent and requires high expertise, thus limiting its wider implementation. Recently, artificial intelligence (AI) has demonstrated potential for applications in UADT videoendoscopy. Among AI methods, deep learning algorithms, and especially convolutional neural networks (CNNs), are particularly suitable for delineating cancers on videoendoscopy. This study is aimed to develop a CNN for automatic semantic segmentation of UADT cancer on endoscopic images.Materials and MethodsA dataset of white light and NBI videoframes of laryngeal squamous cell carcinoma (LSCC) was collected and manually annotated. A novel DL segmentation model (SegMENT) was designed. SegMENT relies on DeepLabV3+ CNN architecture, modified using Xception as a backbone and incorporating ensemble features from other CNNs. The performance of SegMENT was compared to state-of-the-art CNNs (UNet, ResUNet, and DeepLabv3). SegMENT was then validated on two external datasets of NBI images of oropharyngeal (OPSCC) and oral cavity SCC (OSCC) obtained from a previously published study. The impact of in-domain transfer learning through an ensemble technique was evaluated on the external datasets.Results219 LSCC patients were retrospectively included in the study. A total of 683 videoframes composed the LSCC dataset, while the external validation cohorts of OPSCC and OCSCC contained 116 and 102 images. On the LSCC dataset, SegMENT outperformed the other DL models, obtaining the following median values: 0.68 intersection over union (IoU), 0.81 dice similarity coefficient (DSC), 0.95 recall, 0.78 precision, 0.97 accuracy. For the OCSCC and OPSCC datasets, results were superior compared to previously published data: the median performance metrics were, respectively, improved as follows: DSC=10.3% and 11.9%, recall=15.0% and 5.1%, precision=17.0% and 14.7%, accuracy=4.1% and 10.3%.ConclusionSegMENT achieved promising performances, showing that automatic tumor segmentation in endoscopic images is feasible even within the highly heterogeneous and complex UADT environment. SegMENT outperformed the previously published results on the external validation cohorts. The model demonstrated potential for improved detection of early tumors, more precise biopsies, and better selection of resection margins.

Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.

Objective:Precise segmentation of human organs and anatomic structures (especially organs at risk, OARs) is the basis and prerequisite for the treatment planning of radiation therapy. In order to ensure rapid and accurate design of radiotherapy treatment planning, an automatic organ segmentation technique was investigated based on deep learning convolutional neural network.MethodA deep learning convolutional neural network (CNN) algorithm called BCDU-Net has been modified and developed further by us. Twenty two thousand CT images and the corresponding organ contours of 17 types delineated manually by experienced physicians from 329 patients were used to train and validate the algorithm. The CT images randomly selected were employed to test the modified BCDU-Net algorithm. The weight parameters of the algorithm model were acquired from the training of the convolutional neural network.ResultThe average Dice similarity coefficient (DSC) of the automatic segmentation and manual segmentation of the human organs of 17 types reached 0.8376, and the best coefficient reached up to 0.9676. It took 1.5–2 s and about 1 h to automatically segment the contours of an organ in an image of the CT dataset for a patient and the 17 organs for the CT dataset with the method developed by us, respectively.ConclusionThe modified deep neural network algorithm could be used to automatically segment human organs of 17 types quickly and accurately. The accuracy and speed of the method meet the requirements of its application in radiotherapy.

Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm3) (P < 0.001). The deep gray matter lesion volumes were significantly higher for positive than for negative involvement of the deep gray matter (5.6 ± 4.6 mm3 vs. 1.0 ± 1.3 mm3, P < 0.001). The mean Dice similarity coefficients were 0.90 and 0.94 for cortical and deep gray matter lesions, respectively. However, the volume of disease extent was not associated with worse overall survival (cortical extent: P = 0.07; deep gray matter extent: P = 0.12).

BackgroundNasopharyngeal carcinoma (NPC), an endemic malignancy in Southeast Asia and southern China, necessitates precise delineation of the gross tumor volume (GTV) for radiotherapy to optimize patient prognosis. Positron emission tomography (PET) imaging offers valuable metabolic insights to guide radiotherapy planning but is hampered by manual GTV segmentation, which is labor-intensive and prone to inter- and intra-observer variability. Conventional segmentation methods are inherently limited, and comparative evaluations of deep learning (DL) frameworks for PET-based primary NPC lesion segmentation remain scarce. Thus, this study aimed to optimize the delineation of primary NPC radiotherapy target volumes by leveraging PET-image-based DL segmentation technology, with the goal of enhancing both the precision and efficiency of lesion delineation.MethodsResearchers retrospectively collected PET imaging data from 212 NPC patients at the Affiliated Hospital of Guangdong Medical University. The patients were randomly divided into training (170 patients) and testing (42 patients) datasets. A radiation oncologist and a nuclear medicine physician collaboratively delineated the cancer lesion boundaries through consensus. After data preprocessing, three DL models (Res-Unet, Nn-Unet, and Nn-Former) were used to automatically segment the lesions. Training was based on the training dataset. During evaluation, the models’ segmentation performance was comprehensively assessed using Dice similarity coefficient (DSC) and Hausdorff distance (HD). A visual analysis of the results was also conducted to intuitively understand the models’ segmentation capabilities.ResultsIn the training set, Nn-Unet achieved the highest DSC of 0.869, whereas in the testing set, its DSC was 0.833. The DSC for Nn-Former hovered around 0.8 in both the training and testing sets. In contrast, Res-Unet demonstrated the lowest DSC values among the three models, particularly in the testing set (DSC =0.794). Statistical analysis revealed that the DSC of Nn-Unet was significantly higher than that of both Res-Unet and Nn-Former in the training set (P<0.01 for both comparisons). Regarding the 95% HD and average surface distance (ASD) values, Nn-Unet outperformed the other two models in both the training and testing sets. However, Res-Unet exhibited a much higher HD value in the testing set compared to the training set. The Loss curves for all models gradually decreased as training progressed, indicating that the models were learning relevant features of NPC lesions. The final Loss value for Res-Unet was approximately 0.7, whereas Nn-Unet and Nn-Former had final Loss values below 0.8.ConclusionsThe PET-image-based automatic segmentation model for the primary tumor of NPC established in this study demonstrates the clinical potential of Nn-Unet in primary lesion of NPC segmentation tasks.

In this paper, we proposed and validated a novel and accurate pipeline for automatically segmenting flaky corneal ulcer areas from fluorescein staining images. The ulcer area was segmented within the cornea by employing a joint method of Otsu and Gaussian Mixture Modeling (GMM). In the GMM based segmentation, the total number of Gaussians was determined intelligently using an information theory based algorithm. And the fluorescein staining images were processed in the HSV color model rather than the original RGB color model, aiming to improve the segmentation results' robustness and accuracy. In the Otsu based segmentation, the images were processed in the grayscale space with Gamma correction being conducted before the Otsu binarization. Afterwards, morphological operations and median filtering were employed to further improve the Otsu segmentation result. The GMM and Otsu segmentation results were then intersected, for which post-processing was conducted by identifying and filling holes through a fast algorithm using priority queues of pixels. The proposed pipeline has been validated on a total of 150 clinical images. Accurate ulcer segmentation results have been obtained, with the mean Dice Similarity Coefficient (DSC) being 0.88 when comparing the automatic segmentation result with the manually-delineated gold standard. For images in the RGB color space, the mean DSC was 0.83, being much lower than that of the images in the HSV color space.

The Morphology and Composition of Paravertebral Muscles Evaluated by MRI Technology are Used to Predict Low Back Pain: A Systematic Review and Meta-analysis.

Epicardial adipose tissue (EAT) is a visceral fat deposit, that’s known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86 mm, 1.98 ± 1.50 mm, 0.37 ± 0.24 mm, and 0.65 ± 0.37 mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.

There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed a fully automatic segmentation method to segment the spine from CT images, and we compared our segmentation results with reference segmentations obtained by well-known methods. We use a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method. Highlights A method to enhance the accuracy of spine segmentation from CT data was proposed. The proposed method uses Convolutional Neural Network via redundant generation of class labels. Experiments show the segmentation accuracy has been enhanced.

HomeRadiology: Imaging CancerVol. 2, No. 2 Previous Research HighlightsFree AccessA New Approach for Automated Image Segmentation of Organs at Risk in Cervical CancerMeredith Jones, Lacey R. McNallyMeredith Jones, Lacey R. McNallyMeredith JonesLacey R. McNallyPublished Online:Mar 27 2020https://doi.org/10.1148/rycan.2020204010MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Take-Away Points■ Major Focus: Development of an automated multiclass image segmentation model to identify organs at risk (OARs) on CT scans in patients with locally advanced cervical cancer, prior to radiation therapy.■ Key Results: The convolutional neural network model outperformed model-based, intensity-based, and atlas-based segmentation algorithms, with comparable results to other deep learning segmentation models while also decreasing the time needed for image segmentation by several thousand-fold.■ Impact: Reduction of intra- and interuser variability imposed by manual OAR delineation can lead to more accurate and efficient radiation therapy planning.The recent article by Liu et al developed an automated multiclass image segmentation model to identify organs at risk (OARs) on CT scans. To plan effective and safe radiation therapy in the context of cervical cancer, a CT scan is often acquired prior to the first treatment. Currently, a radiation oncologist will segment the CT images by hand, which is a time-consuming process, subject to intra- and interobserver variability, to identify OARs. To overcome these potential limitations and biases, automated image segmentation techniques can be utilized to improve accuracy and speed of annotating each image. A major problem in creation of deep learning image segmentation models for medical images is the lack of available datasets for model training and validation. Liu et al developed a two-dimensional image segmentation model based off the established convolutional neural network architecture, U-Net. U-Net was utilized because it is feasible to train from scratch with a small training set.This study looked at the CT scans of 105 patients with locally advanced cervical cancer. OAR segmentation for cervical cancer is a particularly challenging problem due to variability in organ size and frequent unclear boundaries. Even an experienced radiologist will have trouble. Seven organs were identified as OARs for cervical cancer radiation therapy: bladder, bone marrow, femoral head left, femoral head right, rectum, small intestine, and spinal cord.The model uses context aggregation blocks where U-Net has convolution layers. These context aggregation blocks use dilation convolution layers with different dilated rates and normal convolution layers with different kernel sizes to yield a low false-positive rate and accurate feature extraction in a large receptive field. A squeeze-extract block was used to ensure proper consideration of organs that are small. A class-weighted cross-entropy loss function was also used to overcome the imbalance in the datasets created by the differences in size and shape of each OAR.A ground truth was available based on the radiation oncologist’s annotations, allowing a Dice similarity coefficient (DSC) and 95th Hausdorff distance (HD) to be calculated and used as an evaluation metric. The DSC and the HD metrics indicated a comparable performance to other deep learning image segmentation models in most of the organs analyzed; inferior results were seen in the rectum and superior results in the intestines. All results were analyzed by an oncologist who determined that greater than 90% of results produced by this model were highly acceptable to be used during radiation therapy planning. To make the model more robust and generalizable, it is necessary to acquire a larger dataset with scans from different sources. Translation of this model to three dimensional may ultimately improve clinical translation.Highlighted ArticleLiu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Physica Medica 2020;69:184-191. doi: https://doi.org/10.1016/j.ejmp.2019.12.008Highlighted ArticleLiu Z, Liu X, Xiao B, et al. Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network. Physica Medica 2020;69:184-191. doi: https://doi.org/10.1016/j.ejmp.2019.12.008 Crossref, Medline, Google ScholarArticle HistoryPublished online: Mar 27 2020 FiguresReferencesRelatedDetailsCited ByJournal of Biomimetics, Biomaterials and Biomedical Engineering, Vol. 49Mathematical Problems in Engineering, Vol. 2020Recommended Articles nnU-Net: Further Automating Biomedical Image AutosegmentationRadiology: Imaging Cancer2021Volume: 3Issue: 1Deep Learning: A Primer for RadiologistsRadioGraphics2017Volume: 37Issue: 7pp. 2113-2131Generative Adversarial Networks: A Primer for RadiologistsRadioGraphics2021Volume: 41Issue: 3pp. 840-857Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Deep Generative Adversarial Networks: Applications in Musculoskeletal ImagingRadiology: Artificial Intelligence2021Volume: 3Issue: 3See More RSNA Education Exhibits A Multidisciplinary Approach for Program Development with Artificial Intelligence in Pancreatic Cancer: How We Fit InDigital Posters2019Abnormal Changes After Radiation Therapy: What Radiologists Should KnowDigital Posters2018Artificial Intelligence in Diagnostic Imaging: Current Applications and Future PerspectiveDigital Posters2019 RSNA Case Collection Acute Radiation EnteropathyRSNA Case Collection2021Small Bowel LymphomaRSNA Case Collection2021Hirschsprung DiseaseRSNA Case Collection2021 Vol. 2, No. 2 Metrics Downloaded 310 times Altmetric Score