title: Multi-scale deep networks and regression forests for direct bi-ventricular volume estimation.
abstract: Direct estimation of cardiac ventricular volumes has become increasingly popular and important in cardiac function analysis due to its effectiveness and efficiency by avoiding an intermediate segmentation step. However, existing methods rely on either intensive user inputs or problematic assumptions. To realize the full capacities of direct estimation, this paper presents a general, fully learning-based framework for direct bi-ventricular volume estimation, which removes user inputs and unreliable assumptions. We formulate bi-ventricular volume estimation as a general regression framework which consists of two main full learning stages: unsupervised cardiac image representation learning by multi-scale deep networks and direct bi-ventricular volume estimation by random forests. By leveraging strengths of generative and discriminant learning, the proposed method produces high correlations of around 0.92 with ground truth by human experts for both the left and right ventricles using a leave-one-subject-out cross validation, and largely outperforms existing direct methods on a larger dataset of 100 subjects including both healthy and diseased cases with twice the number of subjects used in previous methods. More importantly, the proposed method can not only be practically used in clinical cardiac function analysis but also be easily extended to other organ volume estimation tasks.
authors.affiliation: The University of Western Ontario, London, ON, Canada; Digital Image Group (DIG), London, ON, Canada.
authors.forename: Xiantong
authors.initials: X
authors.lastname: Zhen
authors.affiliation: GE Healthcare, London, ON, Canada.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Wang
authors.affiliation: St. Joseph's Health Care, London, ON, Canada.
authors.forename: Ali
authors.initials: A
authors.lastname: Islam
authors.affiliation: London Healthcare Sciences Centre, London, ON, Canada.
authors.forename: Mousumi
authors.initials: M
authors.lastname: Bhaduri
authors.affiliation: London Healthcare Sciences Centre, London, ON, Canada.
authors.forename: Ian
authors.initials: I
authors.lastname: Chan
authors.affiliation: The University of Western Ontario, London, ON, Canada; Digital Image Group (DIG), London, ON, Canada; GE Healthcare, London, ON, Canada. Electronic address: shuo.li@ge.com.
authors.forename: Shuo
authors.initials: S
authors.lastname: Li
mesh_terms: D000465:Algorithms; D006352:Heart Ventricles / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D000069550:Machine Learning; D016571:Neural Networks, Computer; D009929:Organ Size; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume; D018754:Ventricular Dysfunction / Q000000981:diagnostic imaging*
keywords: Direct volume estimation; Multi-scale deep networks; Random forests; Regression