5/15/2023 0 Comments Make histogram fityk![]() We also developed and tested an automated technique (LEAP LEsion Automated Preprocessing) that fills lesions with normal WM values before segmentation to ascertain whether it would lead to better segmentation results, as assessed by measures of tissue volumes in the healthy brain. In the second part of the study, which aimed to limit lesion associated segmentation biases, we tested existing methods that either exclude lesions during segmentation, or reclassify lesions as WM after segmentation (for example, Ref. In the first part of the study we developed a technique to simulate focal WM lesions and then quantified their effect on tissue volumes estimated using one frequently employed method, SPM. ![]() There has previously been relatively little work on the simulation of WM lesions ( 4, 9- 11), their effect on GM and WM tissue volumes ( 4), and methods to limit their potential influence on image segmentation Sdika and Pelletier ( 11) have recently looked at the effects of WM lesions on image registration-a key stage in many segmentation algorithms-and developed a lesion filling technique to reduce lesion-associated registration errors. To address these issues we performed a study in two parts: the first to quantify the potential effect of simulated WM lesions on GM and WM atrophy measures, and the second to develop methods to reduce lesion-induced segmentation errors. A clear awareness of a given segmentation method's limitations and robustness in the presence of WM lesions is required to achieve better segmentation solutions, develop practical methods for correcting the effect of lesions on tissue volume estimates, and enable robust interpretation of results derived from segmented GM and WM. 7, 8) that in turn cause a real disease-induced change in GM and WM tissue volumes. 5, 6) and normal-appearing WM (for example, Refs. This can be problematic in multiple sclerosis (MS), where WM lesions are a cardinal feature of the disease, but where important pathological changes also occur in GM (for example, Refs. However, they can be influenced by the presence of WM lesions, leading to misclassifications of GM and WM tissues ( 4). Near fully automated methods are now usually preferred (for example, SPM ( 1), SIENAX ( 2), or FreeSurfer ( 3)). SEPARATION OF BRAIN GRAY MATTER (GM) and white matter (WM) tissues by segmentation of high-resolution T 1-weighted structural images has become a key element of many magnetic resonance imaging (MRI) analysis protocols, and is used to determine tissue volumes and regions of a given tissue type for subsequent quantitative parameter extraction. ![]() Lesion filling with values approximating normal WM enables more accurate GM and WM volume measures and should be applicable to structural scans independently of the software used for the segmentation. The effect of WM lesions on automated GM and WM volume measures may be considerable and thereby obscure real disease-mediated volume changes. ![]() Lesion filling reduced these errors to ≈0.1%. With simulated lesion volumes of 15 mL at 70% of normal WM intensity, the effect was to increase GM fractional (relative to intracranial) volumes by ≈2.3%, and reduce WM fractions by ≈3.6%. GM and WM tissue volume estimates were affected by the presence of WM lesions. ![]() We tested the effects of simulated lesions and lesion-filling correction with LEAP on SPM-derived tissue volume estimates. Simulated lesions with differing volumes and signal intensities were added to volumetric brain images from three healthy subjects and then automatically filled with values approximating normal WM. To develop an automated lesion-filling technique (LEAP LEsion Automated Preprocessing) that would reduce lesion-associated brain tissue segmentation bias (which is known to affect automated brain gray and white matter tissue segmentations in people who have multiple sclerosis), and a WM lesion simulation tool with which to test it. ![]()
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