Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

Consensus Guidelines for Delineation of Clinical Target Volume for Intensity-Modulated Pelvic Radiation Therapy in Postoperative Treatment of Endometrial and Cervical Cancer- An Update

Background and Objectives: This paper evaluates the performance of a variational level set method for performing label fusion through the use of a penalty term, label fusion term, and length regularization term, which automatically labels objects of interest in biomedical images. This paper is an extension of our preliminary work in the conference paper. We mainly focus on the validation of the variational level set method. Subjects and Methods: Label fusion is achieved by combining the three terms: label fusion term, image data term, and regularization term. The curve evolution derived from the energy minimization is impacted by the three terms simultaneously to achieve optimal label fusion. Each label obtained from the nonlinear registration method is represented by a level set function whose zero level contour encloses the labeled region. In Lu et al.'s paper, they employ the level set formulation only for hippocampus segmentation. Results: Our method is compared with majority voting (MV), local weighted voting (LWV), and Simultaneous Truth and Performance Level Estimation (STAPLE). The method is evaluated on MICCAI 2012 Multi-Atlas Labeling challenge and MICCAI 2012 ventricle segmentation challenge. The mean Dice metric is computed using different atlases and produces results with 0.85 for the hippocampus, 0.77 for the amygdala, 0.87 for the caudate, 0.78 for the pallidum, 0.89 for the putamen, 0.91 for the thalamus, and 0.78 for cardiac left ventricles. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms MV, LWV, and STAPLE. The image data term plays a key role in improving the segmentation accuracy. Our method can obtain satisfactory results with fewer atlases.

Corona Virus Disease 2019 (COVID-19) has high infection rate, leading to rapid spread worldwide. Computed tomography (CT) imaging technology is one of the important COVID-19 detection ways and it could provide the physicians diagnosis with major basis. To address the problems that traditional convolutional neural network (CNN) could not capture the spatial information relationship between different features and the shortcoming of original Capsule Network extracting insufficient depth features, this paper proposes a novel optimized residual capsule COVID-19 detection model, named as CO-RCNet and achieves the classification for COVID-19 and non-COVID-19 CT chest scanning images. To demonstrate the advantages and effectiveness of our CO-RCNet model, we perform the ablation experiments to compare with the state-of-the-art COVID-19 detection approaches. The results shows that our method has the highest evaluation metrics and a detection accuracy of 0.9940. That is to say, our method has the best performance. Therefore, the proposed CO-RCNet model can be utilized as the effective COVID-19 detection tool.

BackgroundManual assessment of bone marrow signal is time-consuming and requires meticulous standardisation to secure adequate precision of findings.ObjectiveWe examined the feasibility of using deep learning for automated segmentation of bone marrow signal in children and adolescents.Materials and methodsWe selected knee images from 95 whole-body MRI examinations of healthy individuals and of children with chronic non-bacterial osteomyelitis, ages 6–18 years, in a longitudinal prospective multi-centre study cohort. Bone marrow signal on T2-weighted Dixon water-only images was divided into three color-coded intensity-levels: 1 = slightly increased; 2 = mildly increased; 3 = moderately to highly increased, up to fluid-like signal. We trained a convolutional neural network on 85 examinations to perform bone marrow segmentation. Four readers manually segmented a test set of 10 examinations and calculated ground truth using simultaneous truth and performance level estimation (STAPLE). We evaluated model and rater performance through Dice similarity coefficient and in consensus.ResultsConsensus score of model performance showed acceptable results for all but one examination. Model performance and reader agreement had highest scores for level-1 signal (median Dice 0.68) and lowest scores for level-3 signal (median Dice 0.40), particularly in examinations where this signal was sparse.ConclusionIt is feasible to develop a deep-learning-based model for automated segmentation of bone marrow signal in children and adolescents. Our model performed poorest for the highest signal intensity in examinations where this signal was sparse. Further improvement requires training on larger and more balanced datasets and validation against ground truth, which should be established by radiologists from several institutions in consensus.

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images

Curative intent management of retroperitoneal sarcoma (RPS) requires gross total resection. Preoperative radiotherapy (RT) often is used as an adjuvant to surgery, but recurrence rates remain high. To enhance RT efficacy with acceptable tolerance, there is interest in delivering "boost doses" of RT to high-risk areas of gross tumor volume (HR GTV) judged to be at risk for positive resection margins. We sought to evaluate variability in HR GTV boost target volume delineation among collaborating sarcoma radiation and surgical oncologist teams. Radiation planning CT scans for three cases of RPS were distributed to seven paired radiation and surgical oncologist teams at six institutions. Teams contoured HR GTV boost volumes for each case. Analysis of contour agreement was performed using the simultaneous truth and performance level estimation (STAPLE) algorithm and kappa statistics. HRGTV boost volume contour agreement between the seven teams was "substantial" or "moderate" for all cases. Agreement was best on the torso wall posteriorly (abutting posterior chest abdominal wall) and medially (abutting ipsilateral para-vertebral space and great vessels). Contours varied more significantly abutting visceral organs due to differing surgical opinions regarding planned partial organ resection. Agreement of RPS HRGTV boost volumes between sarcoma radiation and surgical oncologist teams was substantial to moderate. Differences were most striking in regions abutting visceral organs, highlighting the importance of collaboration between the radiation and surgical oncologist for "individualized" target delineation on the basis of areas deemed at risk and planned resection.

GEC-ESTRO (ACROP)–ABS–CBG Consensus Brachytherapy Target Definition Guidelines for Recurrent Endometrial and Cervical Tumors in the Vagina

Consensus Contouring Guidelines for Postoperative Completely Resected Cavity Stereotactic Radiosurgery for Brain Metastases

Variability of gross tumour volume delineation: MRI and CT based tumour and lymph node delineation for lung radiotherapy

BackgroundQuantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard.MethodThe validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression.ResultMean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®’s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2).ConclusionQyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response.

Consensus Delineation Guidelines for Pelvic Lymph Node Radiation Therapy of Prostate Cancer: On Behalf of the Francophone Group of Urological Radiation Therapy (GFRU)

Objectives. Contouring similarity metrics are often used in studies of inter-observer variation and automatic segmentation but do not provide an assessment of clinical impact. This study focused on post-prostatectomy radiotherapy and aimed to (1) identify if there is a relationship between variations in commonly used contouring similarity metrics and resulting dosimetry and (2) identify the variation in clinical target volume (CTV) contouring that significantly impacts dosimetry. Approach. The study retrospectively analysed CT scans of 10 patients from the TROG 08.03 RAVES trial. The CTV, rectum, and bladder were contoured independently by three experienced observers. Using these contours reference simultaneous truth and performance level estimation (STAPLE) volumes were established. Additional CTVs were generated using an atlas algorithm based on a single benchmark case with 42 manual contours. Volumetric-modulated arc therapy (VMAT) treatment plans were generated for the observer, atlas, and reference volumes. The dosimetry was evaluated using radiobiological metrics. Correlations between contouring similarity and dosimetry metrics were calculated using Spearman coefficient (Γ). To access impact of variations in planning target volume (PTV) margin, the STAPLE PTV was uniformly contracted and expanded, with plans created for each PTV volume. STAPLE dose-volume histograms (DVHs) were exported for plans generated based on the contracted/expanded volumes, and dose-volume metrics assessed. Main results. The study found no strong correlations between the considered similarity metrics and modelled outcomes. Moderate correlations (0.5 < ||Γ < 0.7) were observed for Dice similarity coefficient, Jaccard, and mean distance to agreement metrics and rectum toxicities. The observations of this study indicate a tendency for variations in CTV contraction/expansion below 5 mm to result in minor dosimetric impacts. Significance. Contouring similarity metrics must be used with caution when interpreting them as indicators of treatment plan variation. For post-prostatectomy VMAT patients, this work showed variations in contours with an expansion/contraction of less than 5 mm did not lead to notable dosimetric differences, this should be explored in a larger dataset to assess generalisability.

PurposeThe prevalence of the coronavirus disease 2019 (COVID-19) pandemic has made a huge impact on global health and the world economy. Easy detection of COVID-19 through any technological tool like a mobile phone can help a lot. In this research, we focus on detecting COVID-19 from X-ray images on Android mobile with the help of Artificial Intelligence (AI).MethodsA convolutional neural network (CNN) model is developed in MATLAB and then converted to the CNN model to TensorFlow Lite (TFLite) model to deploy on Android mobile. An Android application is developed which uses the TFLite model to detect COVID-19 using X-ray images.ResultsBy employing a 5-fold cross-validation, an average accuracy of 98.65%, sensitivity of 98.49%, specificity of 98.82%, precision of 98.81%, and F1-score of 98.65% are achieved in COVID-19 detection.ConclusionWith our developed Android application, users can detect COVID-19 from X-ray images on Android mobile, and it will be helpful for the diagnosis of COVID-19.

The research community has recently shown significant interest in designing automated systems to detect coronavirus disease 2019 (COVID-19) using deep learning approaches and chest radiography images. However, state-of-the-art deep learning techniques, especially convolutional neural networks (CNNs), demand more learnable parameters and memory. Therefore, they may not be suitable for real-time diagnosis. Thus, the design of a lightweight CNN model for fast and accurate COVID-19 detection is an urgent need. In this paper, a lightweight CNN model called LW-CORONet is proposed that comprises a sequence of convolution, rectified linear unit (ReLU), and pooling layers followed by two fully connected layers. The proposed model facilitates extracting meaningful features from the chest X-ray (CXR) images with only five learnable layers. The proposed model is evaluated using two larger CXR datasets (Dataset-1: 2250 images and Dataset-2: 15,999 images) and the classification accuracy obtained are 98.67% and 99.00% on Dataset-1 and 95.67% and 96.25% on Dataset-2 for multi-class and binary classification cases, respectively. The results are compared with four contemporary pre-trained CNN models as well as state-of-the-art models. The effect of several hyperparameters: different optimization techniques, batch size, and learning rate have also been investigated. The proposed model demands fewer parameters and requires less memory space. Hence, it is effective for COVID-19 detection and can be utilized as a supplementary tool to assist radiologists in their diagnosis.

.Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images.Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model’s ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC).Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images.Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.