A Feasibility Study of Automatic Multi-Organ Segmentation Using Probabilistic Atlas

Thoracic and abdominal multi-organ segmentation has been a challenging problem due to the inter-subject variance of human thoraxes and abdomens as well as the complex 3D intra-subject variance among organs. In this paper, we present a preliminary method for automatically segmenting multiple organs using non-enhanced CT data. The method is based on a simple framework using generic tools and requires no organ-specific prior knowledge. Specifically, we constructed a grayscale CT volume along with a probabilistic atlas consisting of six thoracic and abdominal organs: lungs (left and right), liver, kidneys (left and right) and spleen. A non-rigid mapping between the grayscale CT volume and a new test volume provided the deformation information for mapping the probabilistic atlas to the test CT volume. The evaluation with the 20 VISCERAL non-enhanced CT dataset showed that the proposed method yielded an average Dice coefficient of over 95% for the lungs, over 90% for the liver, as well as around 80% and 70% for the spleen and the kidneys respectively

[1]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[2]  Allan Hanbury,et al.  VISCERAL: Towards Large Data in Medical Imaging - Challenges and Directions , 2012, MCBR-CDS.

[3]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[4]  Max A. Viergever,et al.  Adaptive Stochastic Gradient Descent Optimisation for Image Registration , 2009, International Journal of Computer Vision.

[5]  Viktor Larsson,et al.  Good Features for Reliable Registration in Multi-Atlas Segmentation , 2015, VISCERAL Challenge@ISBI.

[6]  Stefan Roth,et al.  Covariance Matrix Adaptation for Multi-objective Optimization , 2007, Evolutionary Computation.

[7]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[8]  J. Hornegger,et al.  Atlas-based linear volume-of-interest (ABL-VOI) image correction , 2013, Medical Imaging.

[9]  Cheng Huang,et al.  Fully Automatic Multi-Organ Segmentation Based on Multi-Boost Learning and Statistical Shape Model Search , 2015, VISCERAL Challenge@ISBI.

[10]  Hyunjin Park,et al.  Construction of an abdominal probabilistic atlas and its application in segmentation , 2003, IEEE Transactions on Medical Imaging.

[11]  Henning Müller,et al.  Hierarchic Anatomical Structure Segmentation Guided by Spatial Correlations (AnatSeg-Gspac): VISCERAL Anatomy3 , 2015, VISCERAL Challenge@ISBI.