TY - GEN
T1 - Liver workbench
T2 - A tool suite for liver and liver tumor segmentation and modeling
AU - Zhou, Jiayin
AU - Xiong, Wei
AU - Ding, Feng
AU - Huang, Weimin
AU - Qi, Tian
AU - Wang, Zhimin
AU - Oo, Thiha
AU - Venkatesh, Sudhakar Kundapur
PY - 2012
Y1 - 2012
N2 - Robust and efficient liver and tumor segmentation segmentation tools from CT images are important for clinical decision-making in liver treatment planning and response evaluation. In this work, we report recent advances in an ongoing project Liver Workbench which aims to provide a suite of tools for the segmentation segmentation, quantification and modeling of various objects in CT images such as the liver, its vessels and tumors. Firstly, a liver segmentation segmentation approach is described. It registers a liver mesh model model to actual image features by adopting noise-insensitive flipping-free mesh deformations. Next, a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained. Finally, an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation. We also report preliminary experimental results.
AB - Robust and efficient liver and tumor segmentation segmentation tools from CT images are important for clinical decision-making in liver treatment planning and response evaluation. In this work, we report recent advances in an ongoing project Liver Workbench which aims to provide a suite of tools for the segmentation segmentation, quantification and modeling of various objects in CT images such as the liver, its vessels and tumors. Firstly, a liver segmentation segmentation approach is described. It registers a liver mesh model model to actual image features by adopting noise-insensitive flipping-free mesh deformations. Next, a propagation learning approach is incorporated into a semi-automatic classification method for robust segmentation segmentation of liver tumors based on liver ROI obtained. Finally, an unbiased probabilistic liver atlas construction technique is adopted to embody the shape and intensity variation to constrain liver segmentation segmentation. We also report preliminary experimental results.
UR - http://www.scopus.com/inward/record.url?scp=84863080684&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863080684&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-25547-2_12
DO - 10.1007/978-3-642-25547-2_12
M3 - Conference contribution
AN - SCOPUS:84863080684
SN - 9783642255465
T3 - Advances in Intelligent and Soft Computing
SP - 193
EP - 208
BT - Advances in Bio-Imaging
A2 - Lomenie, Nicolas
A2 - Racoceanu, Daniel
A2 - Gouaillard, Alexandre
ER -