Hieu Trung Huynh1, Ngoc Le-Trong2,3, Pham The Bao3,4, Aytek Oto5, Kenji Suzuki6. 1. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam. hthieu@ieee.org. 2. Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam. 3. Faculty of Information Technology, University of Science, Ho Chi Minh City, Vietnam. 4. Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam. 5. Department of Radiology, The University of Chicago, Chicago, IL, 60637, USA. 6. Medical Imaging Research Center, Illinois Institute of Technology, Chicago, IL, 60616, USA.
Abstract
PURPOSE: Our purpose is to develop a fully automated scheme for liver volume measurement in abdominal MR images, without requiring any user input or interaction. METHODS: The proposed scheme is fully automatic for liver volumetry from 3D abdominal MR images, and it consists of three main stages: preprocessing, rough liver shape generation, and liver extraction. The preprocessing stage reduced noise and enhanced the liver boundaries in 3D abdominal MR images. The rough liver shape was revealed fully automatically by using the watershed segmentation, thresholding transform, morphological operations, and statistical properties of the liver. An active contour model was applied to refine the rough liver shape to precisely obtain the liver boundaries. The liver volumes calculated by the proposed scheme were compared to the "gold standard" references which were estimated by an expert abdominal radiologist. RESULTS: The liver volumes computed by using our developed scheme excellently agreed (Intra-class correlation coefficient was 0.94) with the "gold standard" manual volumes by the radiologist in the evaluation with 27 cases from multiple medical centers. The running time was 8.4 min per case on average. CONCLUSIONS: We developed a fully automated liver volumetry scheme in MR, which does not require any interaction by users. It was evaluated with cases from multiple medical centers. The liver volumetry performance of our developed system was comparable to that of the gold standard manual volumetry, and it saved radiologists' time for manual liver volumetry of 24.7 min per case.
PURPOSE: Our purpose is to develop a fully automated scheme for liver volume measurement in abdominal MR images, without requiring any user input or interaction. METHODS: The proposed scheme is fully automatic for liver volumetry from 3D abdominal MR images, and it consists of three main stages: preprocessing, rough liver shape generation, and liver extraction. The preprocessing stage reduced noise and enhanced the liver boundaries in 3D abdominal MR images. The rough liver shape was revealed fully automatically by using the watershed segmentation, thresholding transform, morphological operations, and statistical properties of the liver. An active contour model was applied to refine the rough liver shape to precisely obtain the liver boundaries. The liver volumes calculated by the proposed scheme were compared to the "gold standard" references which were estimated by an expert abdominal radiologist. RESULTS: The liver volumes computed by using our developed scheme excellently agreed (Intra-class correlation coefficient was 0.94) with the "gold standard" manual volumes by the radiologist in the evaluation with 27 cases from multiple medical centers. The running time was 8.4 min per case on average. CONCLUSIONS: We developed a fully automated liver volumetry scheme in MR, which does not require any interaction by users. It was evaluated with cases from multiple medical centers. The liver volumetry performance of our developed system was comparable to that of the gold standard manual volumetry, and it saved radiologists' time for manual liver volumetry of 24.7 min per case.
Authors: A Radtke; G C Sotiropoulos; S Nadalin; E P Molmenti; T Schroeder; H Lang; F Saner; C Valentin-Gamazo; A Frilling; A Schenk; C E Broelsch; M Malagó Journal: Am J Transplant Date: 2007-01-04 Impact factor: 8.086
Authors: Kenji Suzuki; Mark L Epstein; Ryan Kohlbrenner; Shailesh Garg; Masatoshi Hori; Aytekin Oto; Richard L Baron Journal: AJR Am J Roentgenol Date: 2011-10 Impact factor: 3.959
Authors: Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana Journal: Acad Radiol Date: 2019-08-10 Impact factor: 3.173
Authors: Mariëlle J A Jansen; Hugo J Kuijf; Maarten Niekel; Wouter B Veldhuis; Frank J Wessels; Max A Viergever; Josien P W Pluim Journal: J Med Imaging (Bellingham) Date: 2019-10-15
Authors: Grzegorz Chlebus; Hans Meine; Smita Thoduka; Nasreddin Abolmaali; Bram van Ginneken; Horst Karl Hahn; Andrea Schenk Journal: PLoS One Date: 2019-05-20 Impact factor: 3.240
Authors: Moritz Gross; Michael Spektor; Ariel Jaffe; Ahmet S Kucukkaya; Simon Iseke; Stefan P Haider; Mario Strazzabosco; Julius Chapiro; John A Onofrey Journal: PLoS One Date: 2021-12-01 Impact factor: 3.240