A WebBased Software for Training and Quality Assessment in the Image Analysis Workflow for Cardiac T1 Mapping MRI Medical practice makes significant use of imaging scans such as Ultrasound or Magnetic Resonance Imaging as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in postprocessing. However, the value of such parameters depends much on the useru0027s variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular MRI methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.

Automated Multisequence Cardiac MRI Segmentation Using Supervised Domain Adaptation Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixellevel annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multisequence CMR images is tedious and subject to inter and intraobserver variations, developing methods that can automatically adapt from one domain to the target domain is of great interest. In this paper, we propose an approach for domain adaptation in multisequence CMR segmentation task using transfer learning that combines multisource image information. We first train an encoderdecoder CNN on T2weighted and balancedSteady State Free Precession (bSSFP) MR images with pixellevel annotation and finetune the same network with a limited number of Late Gadolinium EnhancedMR (LGEMR) subjects, to adapt the domain features. The domainadapted network was trained with just four LGEMR training samples and obtained an average Dice score of (sim )85.0% on the test set comprises of 40 LGEMR subjects. The proposed method significantly outperformed a network without adaptation trained from scratch on the same set of LGEMR training data.