Landmark Detection in Cardiac MRI Using a Convolutional Neural Network

Purpose: To develop a convolutional neural network (CNN) solution for landmark detection in cardiac MRI. / Materials and Methods: This retrospective study included cine, late-gadolinium enhancement (LGE), and T1 mapping scans from two hospitals. The training set included 2329 patients (34019 images; mean age 54.1 years; 1471 men; December 2017-March 2020). A hold-out test set included 531 patients (7723 images; mean age 51.5 years, 323 men; May 2020-July 2020). CNN models were developed to detect two mitral valve plane and apical points on long-axis images. On short-axis images, anterior and posterior right ventricular insertion points and left ventricle center were detected. Model outputs were compared with manual labels by two readers. The trained model was deployed to MR scanners. / Results: For the long-axis images, successful detection of cardiac landmarks ranged from 99.7% to 100% for cine images and from 99.2% to 99.5% for LGE images. For the short-axis, detection rates was 96.6% for cine, 97.6% for LGE, and 98.9% for T1-mapping. The Euclidean distances between model and manual labels ranged from 2 to 3.5 mm for different landmarks, indicating close agreement between model landmarks to manual labels. No differences were found for the anterior right ventricular insertion angle and left ventricle length by the models and readers for all views and imaging sequences. Model inference on MR scanner took 610 msec on the graphics processing unit and 5.6 sec on central processing unit, respectively, for a typical cardiac cine series. / Conclusion: A CNN was developed for landmark detection in both long and short-axis cardiac MR images for cine, LGE and T1 mapping sequences, with the accuracy comparable to the interreader variation

Computer Assisted Image Labeling for Object Detection Using Deep Learning

Deep learning-based object detectors have shown outstanding performance with state-of-the-art results on public benchmarks. However, they typically consist of millions of parameters and require a large number of training samples to tune these parameters appropriately. These samples are labeled by human annotators, which is a tedious, time-consuming, and expensive process. Moreover, object detectors have high computational costs both for the training and inference phase. This dissertation considers these two aspects of training and deploying deep learning object detectors. First, we study data labeling for the training phase and the robustness of object detectors towards label noise. We classify possible label noise scenarios in 2D object detection and study the sensitivity of one-stage object detectors to label noise in the training phase. We then propose methods for efficient bounding box annotation by utilizing human-machine collaboration. Extensive experiments have been done to study an efficient and effective bounding box annotation scheme for deep learning object detectors. Additionally, we created an easy-to-use, medium-sized, multiclass, fully labeled object detection dataset from indoor premises and released it publicly for registration-free use. Second, we study the practical problem of object detection network deployment with an efficient implementation of the object detection network for applications such as facial analysis, human detection and tracking, and the path prediction of mobile objects on resource-limited devices. We implemented object detection in an image processing pipeline integrating with other tasks for multiple applications and studied the optimal design process. We present the details of the system-level design to incorporate a multitasking network efficiently with the proper system architecture design