Lesion identification using unified segmentation-normalisation models and fuzzy clustering

Abstract

In this paper, we propose a new automated procedure for lesion identification from single images based on the detection of outlier voxels. We demonstrate the utility of this procedure using artificial and real lesions. The scheme rests on two innovations: First, we augment the generative model used for combined segmentation and normalization of images, with an empirical prior for an atypical tissue class, which can be optimised iteratively. Second, we adopt a fuzzy clustering procedure to identify outlier voxels in normalised gray and white matter segments. These two advances suppress misclassification of voxels and restrict lesion identification to gray/white matter lesions respectively. Our analyses show a high sensitivity for detecting and delineating brain lesions with different sizes, locations, and textures. Our approach has important implications for the generation of lesion overlap maps of a given population and the assessment of lesion-deficit mappings. From a clinical perspective, our method should help to compute the total volume of lesion or to trace precisely lesion boundaries that might be pertinent for surgical or diagnostic purposes.

Adaptive weighted fusion of multiple MR sequences for brain lesion segmentation

“…Typically, this is performed manually by a medical expert, however automatic methods have been proposed (see [13] for review) to alleviate the tedious, time consuming and subjective nature of manual delineation. Automated or semi-automated brain lesion detection methods can be classified according to their use of multiple sequences, a priori knowledge about the structure of normal brain, tissue segmentation models, and whether or not specific lesion types are targeted.…”

Quantitative Assessment of Chronic Thalamic Stroke

“…1 However, normalizing brain images of patients onto a standard space defined for functional imaging lacks the resolution required to assess the involvement of small lesioned structures. Additionally, normalization of paraventricular lesions poses significant problems.…”

“…Additionally, normalization of paraventricular lesions poses significant problems. 1,2 In the postacute phase of ischemic disease, the surviving tissue may shrink 3,4 and a ventricular enlargement may take place secondary to the ischemic episode. 5,6 These local changes have not been addressed in previous lesion-symptom mapping studies.…”

Automated detection and classification of brain injury lesions on structural magnetic resonance imaging

“…Regarding the question about which MRI acquisition sequence is better, a conclusive answer has not been found, since it strongly depends on the decision of the clinician and his/her clinical goal (as shown in Table I.5). However, most of the reviewed research papers use T1w or T2w MRI studies (Stamatakis, Tyler 2005); (Shen, Szameitat & Sterr 2008, Seghier et al 2008a, Senyukova et al 2011, Kruggel, Paul & Gertz 2008b, de Boer et al 2009b, Bianchi et al 2014a). In order to understand the role of these two imaging modalities in TBI and stroke, in the next paragraphs the common use of CT and MRI is explained.…”

“…However, the algorithms that have been developed for lesion detection on TBI and stroke are less numerous. Most of these algorithms are based on segmentation methods (Stamatakis, Tyler 2005); (Shen, Szameitat & Sterr 2008, Seghier et al 2008a, Senyukova et al 2011, Kruggel, Paul & Gertz 2008b, de Boer et al 2009b, Bianchi et al 2014a or methods for extracting biomarkers (Bo Wang et al 2013). Almost all of the previous research works use T1 and/or T2 magnetic resonance imaging (MRI) studies.…”