A multi-object statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation

Statistical Atlases have played an important role towards automated medical image segmentation. However, a challenge has been to make the atlas more adaptable to possible errors in deformable registration of anomalous images, given that the body structures of interest for segmentation might present significant differences in shape and texture. Recently, deformable registration errors have been accounted by a method that locally translates the statistical atlas over the test image, after registration, and evaluates candidate objects from a delineation algorithm in order to choose the best one as final segmentation. In this paper, we improve its delineation algorithm and extend the model to be a multi-object statistical atlas, built from control images and adaptable to anomalous images, by incorporating a texture classifier. In order to provide a first proof of concept, we instantiate the new method for segmenting, object-by-object and all objects simultaneously, the left and right brain hemispheres, and the cerebellum, without the brainstem, and evaluate it on MRT1-images of epilepsy patients before and after brain surgery, which removed portions of the temporal lobe. The results show efficiency gain with statistically significant higher accuracy, using the mean Average Symmetric Surface Distance, with respect to the original approach.

[1]  Lucy A. C. Mansilla,et al.  Oriented Image Foresting Transform Segmentation by Seed Competition , 2014, IEEE Transactions on Image Processing.

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

[3]  Jean Meunier,et al.  Average Brain Models: A Convergence Study , 2000, Comput. Vis. Image Underst..

[4]  Arthur W. Toga,et al.  A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development The International Consortium for Brain Mapping (ICBM) , 1995, NeuroImage.

[5]  Jennifer L. Cuzzocreo,et al.  Segmentation of Brain Images Using Adaptive Atlases with Application to Ventriculomegaly , 2011, IPMI.

[6]  Anderson Rocha,et al.  Medical image registration based on watershed transform from greyscale marker and multi-scale parameter search , 2017, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[7]  Max A. Viergever,et al.  Combined pixel classification and atlas-based segmentation of the ventricular system in brain CT Images , 2013, Medical Imaging.

[8]  Dimitris N. Metaxas,et al.  Accurate Whole-Brain Segmentation for Alzheimer's Disease Combining an Adaptive Statistical Atlas and Multi-atlas , 2013, MCV.

[9]  João Paulo Papa,et al.  Efficient supervised optimum-path forest classification for large datasets , 2012, Pattern Recognit..

[10]  Jayaram K. Udupa,et al.  Clouds: A model for synergistic image segmentation , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[11]  Alexandre X Falcão,et al.  Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models. , 2015, Medical physics.

[12]  Marc Modat,et al.  LoAd: A locally adaptive cortical segmentation algorithm , 2011, NeuroImage.

[13]  Ron Kikinis,et al.  Improved watershed transform for medical image segmentation using prior information , 2004, IEEE Transactions on Medical Imaging.