Automatic white matter lesion segmentation using contrast enhanced FLAIR intensity and Markov Random Field

Abstract

White matter lesions (WMLs) are small groups of dead cells that clump together in the white matter of brain. In this paper, we propose a reliable method to automatically segment WMLs. Our method uses a novel filter to enhance the intensity of WMLs. Then a feature set containing enhanced intensity, anatomical and spatial information is used to train a random forest classifier for the initial segmentation of WMLs. Following that a reliable and robust edge potential function based Markov Random Field (MRF) is proposed to obtain the final segmentation by removing false positive WMLs. Quantitative evaluation of the proposed method is performed on 24 subjects of ENVISion study. The segmentation results are validated against the manual segmentation, performed under the supervision of an expert neuroradiologist. The results show a dice similarity index of 0.76 for severe lesion load, 0.73 for moderate lesion load and 0.61 for mild lesion load. In addition to that we have compared our method with three state of the art methods on 20 subjects of Medical Image Computing and Computer Aided Intervention Society's (MICCAI's) MS lesion challenge dataset, where our method shows better segmentation accuracy compare to the state of the art methods. These results indicate that the proposed method can assist the neuroradiologists in assessing the WMLs in clinical practice.

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

“…Computer vision and pattern recognition have already been successfully applied to computer-aided diagnosis using MRI [17,18] and for segmentation of brain structures or lesions [19][20][21]. It has also been used to assess markers of SVD qualitatively.…”

Comparing Two Processing Pipelines to Measure Subcortical and Cortical Volumes in Patients with and without Mild Traumatic Brain Injury

“…Neuroimaging technology has made rapid advances over the past two decades revealing additional information about a number of neurologic processes. In particular, magnetic resonance imaging (MRI) has seen technological improvements in resolution, speed of acquisition, the addition of new sequences, and 3‐dimensional visualization of complex anatomy . Combined, these improvements have changed the way images are used in clinical settings and there are many efforts to establish more standardized evidence‐based uses that will inform clinical understanding of the extent, location, type, progression, and prognosis of an injury or disease process.…”

“…In particular, magnetic resonance imaging (MRI) has seen technological improvements in resolution, speed of acquisition, the addition of new sequences, and 3-dimensional visualization of complex anatomy. [1][2][3][4][5] Combined, these improvements have changed the way images are used in clinical settings and there are many efforts to establish more standardized evidence-based uses that will inform clinical understanding of the extent, location, type, progression, and prognosis of an injury or disease process. Current clinical use of medical imaging requires visual inspection and clinical judgment of competently trained radiologists.…”

Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images using a Convolutional Neural Network Scheme

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