Dr. Pascale Beliveau Profile

Dr. Pascale Beliveau

Postdoctoral Researcher

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Publications (2)

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Proceedings Article | 15 February 2021 Presentation + Paper

Automation of population-based recurrence map for PSMA-PET prostate cancer patients after prostatectomy

Bastien Rigaud, Pascale Béliveau, Guillaume Cazoulat, Daniel Juneau, Cynthia Ménard, Kristy Brock

Proceedings Volume 11596, 115961C (2021) https://doi.org/10.1117/12.2581858

KEYWORDS: Prostate cancer, Veins, Rectum, Bladder, Arteries, Tumor growth modeling, Image segmentation, Image registration, Computed tomography, Cervical cancer

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Purpose Investigate and evaluate the accuracy of deep learning (DL)-based segmentation and deformable image registration (DIR) for the automatization of recurrence risk map atlas definition. Materials and methods Twelve patients with visible recurrence on 18F-DCFPyL PET/CT after prostatectomy were retrospectively analyzed. The bladder, rectum, iliac arteries and veins, and recurrence sites were manually delineated. A previously trained DL model for female pelvic anatomy was re-optimized for male to automatically segment the anatomical regions of interest (ROI). Inter-patient registration was investigated using 4 registration methods: rigid, B-Spline Plastimatch, intensity DIR, and a hybrid intensity-based DIR with varying number of controlling ROI. Performance of the methods were reported using contour-based metrics, determinant of the Jacobian, contour variability in term of volume and position, and probability of overlap with the template organs. Results Transfer learning of the DL model provided greater accuracy for the bladder and rectum than for new structures such as iliac arteries and veins with average Dice similarity coefficient ranges of 0.82-0.96 and 0.63-0.77, respectively. Compared to intensity only DIR, hybrid intensity-based DIR with controlling ROI provided better contour-based metrics, determinant of Jacobian, and less incidence of overlap between recurrence sites and template organs. Centroid position variability between the registration approaches were reported with average range of 1.6-11.3 mm and up to 5.7-30 mm. Conclusion DL and hybrid DIR models can be used to automatize inter-patient registration in the definition of population-based recurrence risk map. DIR uncertainties in the propagation of the recurrence between patients need to be carefully verified before being used in population-based model.

Proceedings Article | 29 March 2007 Paper

Patient specific coronary territory maps

Pascale Beliveau, Randolph Setser, Farida Cheriet, Thomas O'Donnell

Proceedings Volume 6511, 65111J (2007) https://doi.org/10.1117/12.711786

KEYWORDS: Arteries, Image segmentation, Spherical lenses, Visualization, Magnetic resonance imaging, Computed tomography, 3D modeling, Visual process modeling, Ultrasonography, Ray tracing

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It is standard practice for physicians to rely on empirical, population based models to define the relationship between regions of left ventricular (LV) myocardium and the coronary arteries which supply them with blood. Physicians use these models to infer the presence and location of disease within the coronary arteries based on the condition of the myocardium within their distribution (which can be established non-invasively using imaging techniques such as ultrasound or magnetic resonance imaging). However, coronary artery anatomy often varies from the assumed model distribution in the individual patient; thus, a non-invasive method to determine the correspondence between coronary artery anatomy and LV myocardium would have immediate clinical impact. This paper introduces an image-based rendering technique for visualizing maps of coronary distribution in a patient-specific approach. From an image volume derived from computed tomography (CT) images, a segmentation of the LV epicardial surface, as well as the paths of the coronary arteries, is obtained. These paths form seed points for a competitive region growing algorithm applied to the surface of the LV. A ray casting procedure in spherical coordinates from the center of the LV is then performed. The cast rays are mapped to a two-dimensional circular based surface forming our coronary distribution map. We applied our technique to a patient with known coronary artery disease and a qualitative evaluation by an expert in coronary cardiac anatomy showed promising results.

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