Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

Abstract

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks

“…Some early works have used a range of methods for this segmentation task, such as Markov random field model (Kabir et al, 2007), level set (Feng et al, 2015), random forest (Mitra et al, 2014) and support vector machine (Maier et al, 2014). However, their accuracy is challenged by the complicated segmentation problem .…”

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

“…While an increasing number of automatic solutions are presented, there are also a number of semi-automatic methods indicating the difficulty of the task. Among the automatic algorithms, only a few employ pattern classification techniques to learn a segmentation function (Prakash et al, 2006; Maier et al, 2014, 2015c) or design probabilistic generative models of the lesion formation (Derntl et al, 2015; Menze et al, 2015; Forbes et al, 2010; Kabir et al, 2007; Martel et al, 1999). …”

Clustering techniques for neuroimaging applications

“…However, sometimes some parameters have to be configured in advance . Supervised notions turn out to reliably achieve a reasonable high accuracy . Sometimes supervised methods are enhanced by unsupervised techniques to achieve even higher accuracy if sufficient labeled training data is available .…”

“…26 Supervised notions turn out to reliably achieve a reasonable high accuracy. 29 Sometimes supervised methods are enhanced by unsupervised techniques to achieve even higher accuracy if sufficient labeled training data is available. 30 Most of the methods still need postprocessing steps because they do not consider global spatial information except for the tissue atlases.…”