S Park1, L C Chu1, E K Fishman1, A L Yuille2, B Vogelstein3, K W Kinzler3, K M Horton1, R H Hruban4, E S Zinreich1, D Fadaei Fouladi1, S Shayesteh1, J Graves1, S Kawamoto5. 1. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, 601N. Caroline Street, Baltimore, MD 21287, USA. 2. Department of Computer Science, Johns Hopkins University, School of Arts and Sciences, Baltimore, MD 21218, USA. 3. Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA; Johns Hopkins University, School of Medicine, Ludwig Center for Cancer Genetics and Therapeutics, Baltimore, MD 21205, USA. 4. Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, School of Medicine, Baltimore, MD 21205, USA. 5. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, 601N. Caroline Street, Baltimore, MD 21287, USA. Electronic address: skawamo1@jhmi.edu.
Abstract
PURPOSE: The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for development of deep learning algorithms for automatic recognition of a normal pancreas. MATERIALS AND METHODS: Dual-phase contrast-enhanced volumetric CT acquired from 2005 to 2009 from potential kidney donors were retrospectively assessed. Four trained human annotators manually and sequentially annotated 22 structures in each datasets, then expert radiologists confirmed the annotation. For efficient annotation and data management, a commercial software package that supports three-dimensional segmentation was used. RESULTS: A total of 1150 dual-phase CT datasets from 575 subjects were annotated. There were 229 men and 346 women (mean age: 45±12years; range: 18-79years). The mean intra-observer intra-subject dual-phase CT volume difference of all annotated structures was 4.27mL (7.65%). The deep network prediction for multi-organ segmentation showed high fidelity with 89.4% and 1.29mm in terms of mean Dice similarity coefficients and mean surface distances, respectively. CONCLUSIONS: A reliable data collection/annotation process for abdominal structures was developed. This process can be used to generate large datasets appropriate for deep learning.
PURPOSE: The purpose of this study was to report procedures developed to annotate abdominal computed tomography (CT) images from subjects without pancreatic disease that will be used as the input for deep convolutional neural networks (DNN) for development of deep learning algorithms for automatic recognition of a normal pancreas. MATERIALS AND METHODS: Dual-phase contrast-enhanced volumetric CT acquired from 2005 to 2009 from potential kidney donors were retrospectively assessed. Four trained human annotators manually and sequentially annotated 22 structures in each datasets, then expert radiologists confirmed the annotation. For efficient annotation and data management, a commercial software package that supports three-dimensional segmentation was used. RESULTS: A total of 1150 dual-phase CT datasets from 575 subjects were annotated. There were 229 men and 346 women (mean age: 45±12years; range: 18-79years). The mean intra-observer intra-subject dual-phase CT volume difference of all annotated structures was 4.27mL (7.65%). The deep network prediction for multi-organ segmentation showed high fidelity with 89.4% and 1.29mm in terms of mean Dice similarity coefficients and mean surface distances, respectively. CONCLUSIONS: A reliable data collection/annotation process for abdominal structures was developed. This process can be used to generate large datasets appropriate for deep learning.
Authors: Bradley Spieler; Carl Sabottke; Ahmed W Moawad; Ahmed M Gabr; Mustafa R Bashir; Richard Kinh Gian Do; Vahid Yaghmai; Radu Rozenberg; Marielia Gerena; Joseph Yacoub; Khaled M Elsayes Journal: Abdom Radiol (NY) Date: 2021-03-31
Authors: Balázs Kui; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Noémi Gede; Áron Vincze; Judit Bajor; Szilárd Gódi; József Czimmer; Imre Szabó; Anita Illés; Patrícia Sarlós; Roland Hágendorn; Gabriella Pár; Mária Papp; Zsuzsanna Vitális; György Kovács; Eszter Fehér; Ildikó Földi; Ferenc Izbéki; László Gajdán; Roland Fejes; Balázs Csaba Németh; Imola Török; Hunor Farkas; Artautas Mickevicius; Ville Sallinen; Shamil Galeev; Elena Ramírez-Maldonado; Andrea Párniczky; Bálint Erőss; Péter Jenő Hegyi; Katalin Márta; Szilárd Váncsa; Robert Sutton; Peter Szatmary; Diane Latawiec; Chris Halloran; Enrique de-Madaria; Elizabeth Pando; Piero Alberti; Maria José Gómez-Jurado; Alina Tantau; Andrea Szentesi; Péter Hegyi Journal: Clin Transl Med Date: 2022-06
Authors: Elizabeth D Thompson; Nicholas J Roberts; Laura D Wood; James R Eshleman; Michael G Goggins; Scott E Kern; Alison P Klein; Ralph H Hruban Journal: Mod Pathol Date: 2020-07-23 Impact factor: 7.842
Authors: José S Enriquez; Yan Chu; Shivanand Pudakalakatti; Kang Lin Hsieh; Duncan Salmon; Prasanta Dutta; Niki Zacharias Millward; Eugene Lurie; Steven Millward; Florencia McAllister; Anirban Maitra; Subrata Sen; Ann Killary; Jian Zhang; Xiaoqian Jiang; Pratip K Bhattacharya; Shayan Shams Journal: JMIR Med Inform Date: 2021-06-17