OBJECTIVE: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. METHODS: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. RESULTS: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. CONCLUSION: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. SIGNIFICANCE: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.
OBJECTIVE: The purpose of this paper is to describe a semiautomated segmentation method for the liver and evaluate its performance on CT-scan and MR images. METHODS: First, an approximate 3-D model of the liver is initialized from a few user-generated contours to globally outline the liver shape. The model is then automatically deformed by a Laplacian mesh optimization scheme until it precisely delineates the patient's liver. A correction tool was implemented to allow the user to improve the segmentation until satisfaction. RESULTS: The proposed method was tested against 30 CT-scans from the SLIVER07 challenge repository and 20 MR studies from the Montreal University Hospital Center, covering a wide spectrum of liver morphologies and pathologies. The average volumetric overlap error was 5.1% for CT and 7.6% for MRI and the average segmentation time was 6 min. CONCLUSION: The obtained results show that the proposed method is efficient, reliable, and could effectively be used routinely in the clinical setting. SIGNIFICANCE: The proposed approach can alleviate the cumbersome and tedious process of slice-wise segmentation required for precise hepatic volumetry, virtual surgery, and treatment planning.
Authors: Dongxiao Li; Wenxiong Zhong; Kofi M Deh; Thanh Nguyen; Martin R Prince; Yi Wang; Pascal Spincemaille Journal: IEEE Trans Biomed Eng Date: 2018-11-12 Impact factor: 4.538
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: Eugene Vorontsov; Milena Cerny; Philippe Régnier; Lisa Di Jorio; Christopher J Pal; Réal Lapointe; Franck Vandenbroucke-Menu; Simon Turcotte; Samuel Kadoury; An Tang Journal: Radiol Artif Intell Date: 2019-03-13