To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging. A total of 12 (6 semi-, 6 full-automatic) methods are evaluated. The semi-automatic segmentation algorithms are based on both traditional iterative models including watershed, fast marching, region growing, active contours and modern techniques including robust statistical segmenter and super-pixels. These methods entail some sort of interaction mechanism such as placing initialization seeds on images or determining a parameter range. The automatic methods are based on deep learning and they include three framework templates (DeepMedic, NiftyNet and U-Net) the first two of which are applied with default parameter sets and the last two involve adapted novel model designs. For 20 living donors (6 training and 12 test datasets), a group of imaging scientists and radiologists created ground truths by performing manual segmentations on contrast material-enhanced CT images. Each segmentation is evaluated using five metrics (i.e. volume overlap and relative volume errors, average/RMS/maximum symmetrical surface distances). The results are mapped to a scoring system and a final grade is calculated by taking their average. Accuracy and repeatability were evaluated using slice by slice comparisons and volumetric analysis. Diversity and complementarity are observed through heatmaps. Majority voting and Simultaneous Truth and Performance Level Estimation (STAPLE) algorithms are utilized to obtain the fusion of the individual results. The top four methods are determined to be automatic deep models having 79.63, 79.46 and 77.15 and 74.50 scores. Intra-user score is determined as 95.14. Overall, deep automatic segmentation outperformed interactive techniques on all metrics. The mean volume of liver of ground truth is found to be 1409.93 mL ± 271.28 mL, while it is calculated as 1342.21 mL ± 231.24 mL using automatic and 1201.26 mL ± 258.13 mL using interactive methods, showing higher accuracy and less variation on behalf of automatic methods. The qualitative analysis of segmentation results showed significant diversity and complementarity enabling the idea of using ensembles to obtain superior results. The fusion of automatic methods reached 83.87 with majority voting and 86.20 using STAPLE that are only slightly less than fusion of all methods that achieved 86.70 (majority voting) and 88.74 (STAPLE). Use of the new deep learning based automatic segmentation algorithms substantially increases the accuracy and repeatability for segmentation and volumetric measurements of liver. Fusion of automatic methods based on ensemble approaches exhibits best results almost without any additional time cost due to potential parallel execution of multiple models.

Because of the widespread use of CT in the diagnosis of COVID 19, indeterminate presentations such as single, few or unilateral lesions amount to a considerable number. We aimed to develop a new classification and structured reporting system on CT imaging (COVID-19 S) that would facilitate the diagnosis of COVID-19 in the most accurate way. Our retrospective cohort included 803 patients with a chest CT scan upon suspicion of COVID 19. The patients' history, physical examination, CT findings, RT PCR, and other laboratory test results were reviewed, and a final diagnosis was made as COVID 19 or non-COVID 19. Chest CT scans were classified according to the COVID 19 S CT diagnosis criteria. Cohen's kappa analysis was used. Final clinical diagnosis was COVID-19 in 98 patients (12%). According to the COVID-19 S CT diagnosis criteria, the number of patients in the normal, compatible with COVID 19, indeterminate and alternative diagnosis groups were 581 (72.3%), 97 (12.1%), 16 (2.0%) and 109 (13.6%). When the indeterminate group was combined with the group compatible with COVID 19, the sensitivity and specificity of COVID-19 S were 99.0% and 87.1%, with 85.8% positive predictive value (PPV) and 99.1% negative predictive value (NPV). When the indeterminate group was combined with the alternative diagnosis group, the sensitivity and specificity of COVID-19 S were 93.9% and 96.0%, with 94.8% PPV and 95.2% NPV. COVID-19 S CT classification system may meet the needs of radiologists in distinguishing COVID-19 from pneumonia of other etiologies and help optimize patient management and disease control in this pandemic by the use of structured reporting.

Magnetic Resonance Imaging (MRI) is an exceptional diagnostic tool known for its ability to provide superior soft tissue contrast. Despite its proven efficacy, traditional MRI methods have some inherent limitations, including relatively longer scan times and the requirement for specialized expertise in data analysis. These factors can, at times, create challenges in the widespread adoption of MRI in certain research and clinical scenarios. Deep learning (DL) methods for MRI reconstruction and analysis offer a promising solution to address this issue. While these DL techniques have been validated using standard image quality metrics, they fall short in assessing clinically relevant details. This hinders their clinical reliability and practical application. Although radiologists have previously compared the diagnostic equivalence of accelerated DL-reconstructed images to conventional ones for evaluating knee internal derangement, it remains uncertain whether DL detectors can accurately identify clinically important details when reconstruction models are used. Furthermore, the artifacts or hallucinations generated by DL reconstructions in knee MRI have not been examined in a clinical setting. Thus, this study aims to determine the performance of DL detectors on retrospectively accelerated DL-reconstructed knee MRI, comparing them to conventional imaging and expert evaluations for detecting meniscal tears. Our investigation yields three significant contributions. First, an in-depth analysis of DL reconstruction highlights the presence of hallucinations in the femur, tibia, and false positive artifacts, indicating that the overall reconstruction quality does not directly affect pathological features. Second, the results of DL detectors demonstrate that their performance aligns well with image quality assessment metrics and expert scores. This finding validates the reliability of the detection outcomes. Finally, we propose an integrated (i.e. reconstruction + detection) process for meniscal tears on fastMRI+ data and achieved state-of-the-art results with average precision scores of 0.69 and 0.67 at 4- and 8-fold accelerations, respectively.