Objective and non-invasive quantification of ischemic stroke and differentiation of salvageable from non-salvageable tissue is critical to treatment planning. However current Magnetic Resonance Imaging(MRI) techniques are time consuming and rely on manual detection methods. Computer aided preliminary screening of the injured tissue could assist neuroradiologists in performing more detailed analysis of the lesion components. An established Hierarchical Region Splitting (HRS) method was extended to segment lesions from adult patients who suffered a clinical stroke using diffusion- and perfusion weighted image (DWI-PWI) maps and associated computed maps. Apart from lesion quantification PWI-DWI based HRS was also able to automatically quantify core (irrecoverable) the penumbra (potentially recoverable) which helped to estimate salvageable tissue. The PWI-DWI/HRS results were validated by comparing with manually demarcated ground truth in terms of performance indices like lesion volume (82.1% accuracy), sensitivity (78.8%), specificity (99.3%) and similarity (78.54%) for a dataset of 10 acute adult stroke patients. Data sets were classified into severe, moderate and mild injuries based on total lesion volume. Proposed PWI-DWI/HRS method demonstrated accuracy close to manual lesion demarcation with high performance indices for core and penumbra in severe and moderate classes.
Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible necrosis typically located at the center of the lesion, while reversible hypoxic changes in the outer regions of the lesion are termed as the penumbra. Early estimation of core and penumbra in ischaemic stroke is crucial for timely intervention with thrombolytic therapy to reverse the damage and restore normalcy. Multisequence magnetic resonance imaging (MRI) is commonly employed for clinical diagnosis. However, a sequence singly has not been found to be sufficiently able to differentiate between core and penumbra, while a combination of sequences is required to determine the extent of the damage. The challenge, however, is that with an increase in the number of sequences, it cognitively taxes the clinician to discover symptomatic biomarkers in these images. In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI. The method employs recent developments in convolutional neural networks (CNN) for semantic segmentation in medical images. In the absence of availability of a large amount of labeled data, the CNN is trained using an adversarial approach employing cross-entropy as a segmentation loss along with losses aggregated from three discriminators of which two employ relativistic visual Turing test. This method is experimentally validated on the ISLES-2015 dataset through three-fold cross-validation to obtain with an average Dice score of 0.82 and 0.73 for segmentation of penumbra and core respectively.
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