A learning-based automatic segmentation and quantification method on left ventricle in gated myocardial perfusion SPECT imaging: A feasibility study

Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?

(2018) Olivier Bernard et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance

(2017) Tuan Anh Ngo et al.   MEDICAL IMAGE ANALYSIS

Automatic Valve Plane Localization in Myocardial Perfusion SPECT/CT by Machine Learning: Anatomic and Clinical Validation

(2016) Julian Betancur et al.   JOURNAL OF NUCLEAR MEDICINE

Nuclear Image-Guided Approaches for Cardiac Resynchronization Therapy (CRT)

(2015) Weihua Zhou et al.   Current Cardiology Reports

Quantitative analysis of perfusion studies: Strengths and pitfalls

(2012) Piotr Slomka et al.   JOURNAL OF NUCLEAR CARDIOLOGY

A Prospective Pilot Study to Evaluate the Relationship Between Acute Change in Left Ventricular Synchrony After Cardiac Resynchronization Therapy and Patient Outcome Using a Single-Injection Gated SPECT Protocol

(2011) Mati Friehling et al.   Circulation-Cardiovascular Imaging

Interpretation of SPECT/CT Myocardial Perfusion Images: Common Artifacts and Quality Control Techniques

(2011) Ryan A. Dvorak et al.   RADIOGRAPHICS

An Improved Method for Automatic Segmentation of the Left Ventricle in Myocardial Perfusion SPECT

(2009) H. Soneson et al.   JOURNAL OF NUCLEAR MEDICINE

Automated Quality Control for Segmentation of Myocardial Perfusion SPECT

(2009) Y. Xu et al.   JOURNAL OF NUCLEAR MEDICINE