A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

Journal: International journal of computer assisted radiology and surgery

Published Date: Jul 20, 2020

Abstract

PURPOSE: This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoperative TRUS images is clinically important to have faster and reproducible interventions that can benefit both the clinical workflow and patient health.

Authors

Kibrom Berihu Girum

ImViA Laboratory, University of Burgundy, Batiment I3M, 64b rue sully, 21000, Dijon, France. kibrom-berihu_girum@etu.u-bourgogne.fr.
Alain Lalande
Raabid Hussain

ImViA Laboratory, University of Burgundy, Batiment I3M, 64b rue sully, 21000, Dijon, France.
Gilles Créhange

LE2I UMR6306, Centre national de la recherche scientifique, Arts et Métiers, Université Bourgogne Franche-Comté, Dijon, France; Department of Radiation Oncology, Centre Georges-François Leclerc, Dijon, France.

Keywords

Algorithms Artifacts Brachytherapy Deep Learning Diagnosis, Computer-Assisted Humans Male Models, Statistical Prostate Prostatic Neoplasms Reproducibility of Results Ultrasonography Workflow

External Resources

View on PubMed Access via DOI PubMed (32691302)

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

Abstract

Authors

Keywords

External Resources

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