PURPOSE: Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed. METHODS: An intensity-based segmentation method that uses probabilistic model to increase the precision of the segmentation was developed. The model was build based on 60 manually contoured liver CT exams and partitioned into 8 parts according to the (Couinaud) segmental anatomy of the liver. The partitioning allows using different intensity statistics in different parts of the organ, which makes it insensitive to local intensity differences from MR artifacts or pathology. The method employs a modality independent model with registration that exploits some LAVA image characteristics. This dependence can be eliminated to adapt the segmentation method for a wide range of MR images. RESULTS: The method was evaluated using eight representative, manually segmented MR LAVA exams. The results show that the method can accurately segment the liver volume despite various MR artifacts and pathology. The evaluation shows that the proposed method provides more precise segmentation (6% average absolute relative volume error) compared with global intensity statistics for the whole organ (20% average absolute relative volume error). The compute time of the method was 30 s in average, which is acceptable for wide range of clinical applications. CONCLUSION: An automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.
PURPOSE: Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed. METHODS: An intensity-based segmentation method that uses probabilistic model to increase the precision of the segmentation was developed. The model was build based on 60 manually contoured liver CT exams and partitioned into 8 parts according to the (Couinaud) segmental anatomy of the liver. The partitioning allows using different intensity statistics in different parts of the organ, which makes it insensitive to local intensity differences from MR artifacts or pathology. The method employs a modality independent model with registration that exploits some LAVA image characteristics. This dependence can be eliminated to adapt the segmentation method for a wide range of MR images. RESULTS: The method was evaluated using eight representative, manually segmented MR LAVA exams. The results show that the method can accurately segment the liver volume despite various MR artifacts and pathology. The evaluation shows that the proposed method provides more precise segmentation (6% average absolute relative volume error) compared with global intensity statistics for the whole organ (20% average absolute relative volume error). The compute time of the method was 30 s in average, which is acceptable for wide range of clinical applications. CONCLUSION: An automatic method that can segment the liver in contrast-enhanced MR LAVA images was developed and tested. The results demonstrate that the method is feasible, efficient and robust to artifacts and pathology.
Authors: Laurent Hermoye; Ismael Laamari-Azjal; Zhujiang Cao; Laurence Annet; Jan Lerut; Benoit M Dawant; Bernard E Van Beers Journal: Radiology Date: 2004-11-24 Impact factor: 11.105
Authors: Tobias Heimann; Bram van Ginneken; Martin A Styner; Yulia Arzhaeva; Volker Aurich; Christian Bauer; Andreas Beck; Christoph Becker; Reinhard Beichel; György Bekes; Fernando Bello; Gerd Binnig; Horst Bischof; Alexander Bornik; Peter M M Cashman; Ying Chi; Andrés Cordova; Benoit M Dawant; Márta Fidrich; Jacob D Furst; Daisuke Furukawa; Lars Grenacher; Joachim Hornegger; Dagmar Kainmüller; Richard I Kitney; Hidefumi Kobatake; Hans Lamecker; Thomas Lange; Jeongjin Lee; Brian Lennon; Rui Li; Senhu Li; Hans-Peter Meinzer; Gábor Nemeth; Daniela S Raicu; Anne-Mareike Rau; Eva M van Rikxoort; Mikaël Rousson; László Rusko; Kinda A Saddi; Günter Schmidt; Dieter Seghers; Akinobu Shimizu; Pieter Slagmolen; Erich Sorantin; Grzegorz Soza; Ruchaneewan Susomboon; Jonathan M Waite; Andreas Wimmer; Ivo Wolf Journal: IEEE Trans Med Imaging Date: 2009-02-10 Impact factor: 10.048
Authors: Akshat Gotra; Lojan Sivakumaran; Gabriel Chartrand; Kim-Nhien Vu; Franck Vandenbroucke-Menu; Claude Kauffmann; Samuel Kadoury; Benoît Gallix; Jacques A de Guise; An Tang Journal: Insights Imaging Date: 2017-06-14