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Semi-automated labelling of medical images: benefits of a collaborative work in the evaluation of prostate cancer in MRI

By Christian Mata, Alain Lalande, Paul Walker, Arnau Oliver and Joan Marti

Abstract

Purpose: The goal of this study is to show the advantage of a collaborative work in the annotation and evaluation of prostate cancer tissues from T2-weighted MRI compared to the commonly used double blind evaluation. Methods: The variability of medical findings focused on the prostate gland (central gland, peripheral and tumoural zones) by two independent experts was firstly evaluated, and secondly compared with a consensus of these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate (peripheral and central zones) and cancer using a semi-automated tool. One of the experts then drew the ROI with knowledge of the other expert's ROI. Results: The surface area of each ROI as the Hausdorff distance and the Dice coefficient for each contour were evaluated between the different experiments, taking the drawing of the second expert as the reference. The results showed that the significant differences between the two experts became non-significant with a collaborative work. Conclusions: This study shows that collaborative work with a dedicated tool allows a better consensus between expertise than using a double blind evaluation. Although we show this for prostate cancer evaluation in T2-weighted MRI, the results of this research can be extrapolated to other diseases and kind of medical images

Topics: [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Publisher: HAL CCSD
Year: 2018
OAI identifier: oai:HAL:hal-01793436v1
Provided by: HAL-uB
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