Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment.

PURPOSE: The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vertebra. MATERIALS AND METHODS: An ensemble of 15 deep learning models with a two-dimensional U-net architecture with a 4-level depth and 18 initial filters were trained to segment MBM. The muscular surface values were computed from the predicted masks and corrected with the algorithm's estimated bias. Resulting mask prediction and surface prediction were assessed using Dice similarity coefficient (DSC) and root mean squared error (RMSE) scores respectively using ground truth masks as standards of reference. RESULTS: A total of 1025 individual CT slices were used for training and validation and 500 additional axial CT slices were used for testing. The obtained mean DSC and RMSE on the test set were 0.97 and 3.7 cm2 respectively. CONCLUSION: Deep learning methods using convolutional neural networks algorithm enable a robust and automated extraction of CT derived MBM for sarcopenia assessment, which could be implemented in a clinical workflow.

Diagnosis of focal liver lesions from ultrasound using deep learning.

PURPOSE: The purpose of this study was to create an algorithm that simultaneously detects and characterizes (benign vs. malignant) focal liver lesion (FLL) using deep learning. MATERIALS AND METHODS: We trained our algorithm on a dataset proposed during a data challenge organized at the 2018 Journées Francophones de Radiologie. The dataset was composed of 367 two-dimensional ultrasound images from 367 individual livers, captured at various institutions. The algorithm was guided using an attention mechanism with annotations made by a radiologist. The algorithm was then tested on a new data set from 177 patients. RESULTS: The models reached mean ROC-AUC scores of 0.935 for FLL detection and 0.916 for FLL characterization over three shuffled three-fold cross-validations performed with the training data. On the new dataset of 177 patients, our models reached a weighted mean ROC-AUC scores of 0.891 for seven different tasks. CONCLUSION: This study that uses a supervised-attention mechanism focused on FLL detection and characterization from liver ultrasound images. This method could prove to be highly relevant for medical imaging once validated on a larger independent cohort.

Detection and characterization of MRI breast lesions using deep learning.

PURPOSE: The purpose of this study was to assess the potential of a deep learning model to discriminate between benign and malignant breast lesions using magnetic resonance imaging (MRI) and characterize different histological subtypes of breast lesions. MATERIALS AND METHODS: We developed a deep learning model that simultaneously learns to detect lesions and characterize them. We created a lesion-characterization model based on a single two-dimensional T1-weighted fat suppressed MR image obtained after intravenous administration of a gadolinium chelate selected by radiologists. The data included 335 MR images from 335 patients, representing 17 different histological subtypes of breast lesions grouped into four categories (mammary gland, benign lesions, invasive ductal carcinoma and other malignant lesions). Algorithm performance was evaluated on an independent test set of 168 MR images using weighted sums of the area under the curve (AUC) scores. RESULTS: We obtained a cross-validation score of 0.817 weighted average receiver operating characteristic (ROC)-AUC on the training set computed as the mean of three-shuffle three-fold cross-validation. Our model reached a weighted mean AUC of 0.816 on the independent challenge test set. CONCLUSION: This study shows good performance of a supervised-attention model with deep learning for breast MRI. This method should be validated on a larger and independent cohort.

Retrospective evaluation of two latex agglutination tests for detection of circulating antigens during invasive candidosis.

Two latex agglutination tests for the detection of Candida antigens, Pastorex Candida (Sanofi Diagnostics Pasteur, Marnes-la-Coquette, France) and Cand-Tec (Ramco Laboratories, Inc., Houston, Tex.), were applied to 79 serum samples from 19 patients who were retrospectively selected on the basis of mycological and clinical evidence of C. albicans infection and the availability of serial serum samples taken near the date of a positive culture. The specificity in 60 control individuals was 100% for Pastorex and 98.3% for Cand-Tec. The tests scored positive for 10 (52.6%) and 9 (47.4%) patients, respectively. Pastorex detected antigen in only 3 of 12 patients (25%) with positive antibody detection tests, but was positive for all 7 patients (100%) who produced no or a low antibody response, suggesting that the test performs better in the absence of antibodies. However, the sensitivity of Pastorex also increased with the number of samples available per patient, which was lower for high-antibody-responder patients (2.8 versus 5.7). If the patients who provided only one or two serum samples were eliminated, the sensitivity of Pastorex rose to 76.9%. For the Cand-Tec, the sensitivity was not related to the presence of antibodies, nor was it related to the number of samples per patient. The observed antigenemia was transient with both Pastorex and Cand-Tec. Only 12.5% of the positive reactions occurred on the same serum sample, confirming that the two tests react with different antigens. A positive antigen test preceded other diagnostic indications for 6 of 10 Pastorex-positive patients and 5 of 9 Cand-Tec-positive patients.