Deep learning model for automatic prostate segmentation on bicentric T2w images with and without endorectal coil

被引:6
作者
Barra, Davide [1 ]
Nicoletti, Giulia [1 ]
Defeudis, Arianna [1 ]
Mazzetti, Simone [1 ]
Panic, Jovana [3 ]
Gatti, Marco [1 ]
Faletti, Riccardo [1 ]
Russo, Filippo [2 ]
Regge, Daniele [1 ,2 ]
Giannini, Valentina [1 ]
机构
[1] Univ Turin, Dept Surg Sci, Via Genova 3, I-10126 Turin, Italy
[2] FPO IRCCS, Candiolo Canc Inst, Str Prov 142,Km 3-95, Candiolo, TO, Italy
[3] Politecn Torino, Dept Elect & Telecommun, Turin, Italy
来源
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) | 2021年
关键词
CANCER; MRI; BIOPSY;
D O I
10.1109/EMBC46164.2021.9630792
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.
引用
收藏
页码:3370 / 3373
页数:4
相关论文
共 20 条
[1]   Update of the Standard Operating Procedure on the Use of Multiparametric Magnetic Resonance Imaging for the Diagnosis, Staging and Management of Prostate Cancer [J].
Bjurlin, Marc A. ;
Carroll, Peter R. ;
Eggener, Scott ;
Fulgham, Pat F. ;
Margolis, Daniel J. ;
Pinto, Peter A. ;
Rosenkrantz, Andrew B. ;
Rubenstein, Jonathan N. ;
Rukstalis, Daniel B. ;
Taneja, Samir S. ;
Turkbey, Baris .
JOURNAL OF UROLOGY, 2020, 203 (04) :706-712
[2]  
Bloch N., 2015, CANC IMAGING ARCH
[3]   Multiparametric MRI to improve detection of prostate cancer compared with transrectal ultrasound-guided prostate biopsy alone: the PROMIS study [J].
Brown, Louise Clare ;
Ahmed, Hashim U. ;
Faria, Rita ;
Bosaily, Ahmed El-Shater ;
Gabe, Rhian ;
Kaplan, Richard S. ;
Parmar, Mahesh ;
Collaco-Moraes, Yolanda ;
Ward, Katie ;
Hindley, Richard Graham ;
Freeman, Alex ;
Kirkham, Alexander ;
Oldroyd, Robert ;
Parker, Chris ;
Bott, Simon ;
Burns-Cox, Nick ;
Dudderidge, Tim ;
Ghei, Maneesh ;
Henderson, Alastair ;
Persad, Rajendra ;
Rosario, Derek J. ;
Shergill, Iqbal ;
Winkler, Mathias ;
Soares, Marta ;
Spackman, Eldon ;
Sculpher, Mark ;
Emberton, Mark .
HEALTH TECHNOLOGY ASSESSMENT, 2018, 22 (39) :1-+
[4]  
Chollet F., 2015, KERAS
[5]  
Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848
[6]  
Fong Yan Kit, 2005, Rev Urol, V7, P63
[7]   Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study [J].
Giannini, Valentina ;
Mazzetti, Simone ;
Armando, Enrico ;
Carabalona, Silvia ;
Russo, Filippo ;
Giacobbe, Alessandro ;
Muto, Giovanni ;
Regge, Daniele .
EUROPEAN RADIOLOGY, 2017, 27 (10) :4200-4208
[8]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[9]   The Accuracy of Different Biopsy Strategies for the Detection of Clinically Important Prostate Cancer: A Computer Simulation [J].
Lecornet, Emilie ;
Ahmed, Hashim Uddin ;
Hu, Yipeng ;
Moore, Caroline M. ;
Nevoux, Pierre ;
Barratt, Dean ;
Hawkes, David ;
Villers, Arnaud ;
Emberton, Mark .
JOURNAL OF UROLOGY, 2012, 188 (03) :974-980
[10]   Focal Loss for Dense Object Detection [J].
Lin, Tsung-Yi ;
Goyal, Priya ;
Girshick, Ross ;
He, Kaiming ;
Dollar, Piotr .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42 (02) :318-327