Gaussian modelling for operator-independent and threshold-free volumetric segmentation of phase sensitive inversion recovery late gadolinium enhanced images
نویسندگان
چکیده
Background Infarct size following STEMI carries prognostic significance. Current CMR methods for measuring abnormal myocardium are based on detecting pixels above a certain intensity threshold. This threshold is often defined from a ROI as the mean plus a number (nSD) of standard deviations. This method assumes that the ROI is “normal” and not affected by disease, and is sensitive both to the choice of the ROI and the nSD multiplier. Objective determination of the volume of damage on LGE images may be desirable.
منابع مشابه
New automatic algorithm for segmentation of myocardial scar in both inversion recovery and phase sensitive inversion recovery late gadolinium enhancement: validation against TTC and in multi-center, multi-vendor patient data
New automatic algorithm for segmentation of myocardial scar in both inversion recovery and phase sensitive inversion recovery late gadolinium enhancement: validation against TTC and in multi-center, multi-vendor patient data Jane Tufvesson, Robert Jablonowski, Henrik Engblom, Marcus Carlsson, Anthony H Aletras, Pavel Hoffmann, Alexis Jacquier, Frank Kober, Bernhard Metzler, David Erlinge, Dan A...
متن کاملSynthetic phase sensitive inversion recovery late gadolinium enhancement from post-contrast T1-mapping shows excellent agreement with conventional PSIR-LGE for diagnosing myocardial scar
Background Cardiac magnetic resonance (CMR) imaging using phase sensitive inversion recovery (PSIR) late gadolinium enhancement (LGE) is the in vivo reference standard for assessing focal myocardial scar. Post-contrast T1-mapping by Modified Look-Locker Inversion recovery (MOLLI) can be used to generate a Synthetic PSIR LGE (SynLGE) image with an image contrast similar to conventional LGE image...
متن کاملMyocardial fibrosis delineation in late gadolinium enhancement images of Hypertrophic Cardiomyopathy patients using deep learning methods
Introduction: Accurate delineation of myocardial fibrosis in Late Gadolinium Enhancement on Cardiac Magnetic Resonance (LGE-CMR) has a crucial role in the assessment and risk stratification of HCM patients. As this is time-consuming and requires expertise, automation can be essential in accelerating this process. This study aims to use Unet-based deep learning methods to automate the mentioned ...
متن کاملAutomatic scar segmentation in dual inversion recovery images is more consistent with manual outlining than in conventional inversion recovery images
Background The dual inversion recovery (dual IR) pulse sequence has recently been shown to improve blood suppression and infarct delineation in late gadolinium enhancement (LGE) images of myocardial infarction. This resulted in significantly lower inter-observer variability in manual outlining of scar and higher expert confidence in scar detection and transmurality when compared with convention...
متن کاملEstimation of tumor volume with fuzzy-connectedness segmentation of MR images.
BACKGROUND AND PURPOSE Reproducible measurements of brain tumor volume are helpful in evaluating the response to therapy and the need for changing treatment plans. Our purpose was to adapt the fuzzy-connectedness segmentation technique to measure tumor volume. This technique requires only limited operator interaction. METHODS Routine postoperative brain MR imaging was performed in 19 patients...
متن کامل