Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features

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Deep Learning for Cardiac Image Segmentation: A Review (2019, methodology): “This framework showed significant improvement over the previous non-DL MSL (218) method while achieving competitive run-time.”
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans (2019, methodology): “Left Kidney Center PBT [3] 1.”
3D/2D model-to-image registration by imitation learning for cardiac procedures (2018, positive): “The CT datasets were automatically segmented by a combination of object localization and a multistep non-rigid deformation estimation [18].”
Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “The first application covered the segmentation of all 4 chambers of the heart from CT images (43).”
A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. (2016, methodology): “Such ML models have been used successfully in many medical imaging applications, including automatic heart isolation (61) and segmentation of different organs (62).”
Combo Loss: Handling Input and Output Imbalance in Multi-Organ Segmentation (2018, neutral): “[1] 2008 MSL CT & MRI X X X X 2D/3D (400, 395) (226, 3F)”
Deep Learning in Medical Ultrasound Analysis: A Review (2019, background): “To address the issue of 3D object detection, marginal space deep learning (MSDL), which takes advantage of marginal space learning (MSL) [149] and deep learning, was adopted.”