Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI-Reference-Cited by-同舟云学术

Reference standard for the evaluation of automatic segmentation algorithms: Quantification of inter observer variability of manual delineation of prostate contour on MRI

Published:2024-02 Issue:2 Volume:105 Page:65-73
ISSN:2211-5684
Container-title:Diagnostic and Interventional Imaging
language:en
Short-container-title:Diagnostic and Interventional Imaging

Author:

Molière Sébastien^ORCID,Hamzaoui Dimitri,Granger Benjamin,Montagne Sarah^ORCID,Allera Alexandre,Ezziane Malek,Luzurier Anna,Quint Raphaelle,Kalai Mehdi,Ayache Nicholas,Delingette Hervé^ORCID,Renard-Penna Raphaële

Publisher

Elsevier BV

Subject

Radiology, Nuclear Medicine and imaging,General Medicine,Radiological and Ultrasound Technology

Reference55 articles.

1. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader;Jaouen;Diagn Interv Imaging,2023

2. Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: how accurate are they when tested on independent cohorts? – a systematic review;Rouvière;Diagn Interv Imaging,2023

3. Artificial intelligence in diagnostic and interventional radiology: where are we now?;Boeken;Diagn Interv Imaging,2023

4. Variability and reproducibility in deep learning for medical image segmentation;Renard;Sci Rep,2020

5. Volumetric measurement of hepatic tumors: accuracy of manual contouring using CT with volumetric pathology as the reference method;Pupulim;Diagn Interv Imaging,2018

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