Online Program Home
My Program

Abstract Details

Activity Number: 598
Type: Topic Contributed
Date/Time: Wednesday, August 3, 2016 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #319060
Title: A Multi-Scale Spatial Model for Stroke Lesion Segmentation
Author(s): Huiyan Sang* and Ciprian Crainiceanu and Elizabeth M. Sweeney and John Muschelli
Companies: Texas A&M University and The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University
Keywords: Clinical image ; Lesion segmentation ; Point process ; Spatial Clustering

Ischemic stroke is the third most frequent cause of death and a major cause of disability in industrial countries. In clinical practice, Diffusion weighted images (DWI), T1-weighted (T1W), T2-weighted (T2W) and fluid attenuated inversion recovery (FLAIR) images are often acquired to diagnose and monitor disease progression of strokes. However, manual segmentation of stroke lesions from these brain images is often a challenging and time consuming task and can only be performed by trained clinicians. In this paper, we propose an automated method to locate, segment and quantify stroke lesion areas using a new bias correction algorithm and a multi-scale 3D spatial point process clustering model. We evaluate the performance of the proposed model using the Ischemic Stroke Lesion Segmentation Challenge data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

Copyright © American Statistical Association