Journal of neuroscience methods · 2020
Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI.
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Nazari-Farsani Sanaz, Nyman Mikko, Karjalainen Tomi, Bucci Marco, Isojärvi Janne, Nummenmaa Lauri
Аннотация
BACKGROUND: Successful delineation of lesions in acute ischemic strokes (AIS) is crucial for increasing the likelihood of good clinical outcome for the patient.
NEW METHODS: We developed a fully automated method to localize and segment AIS lesions in variable locations for 192 multimodal 3D-magnetic resonance images (MRI) including 106 stroke and 86 healthy cases. The method works based on the Crawford-Howell t-test and comparison of stroke images to healthy controls. We then developed a classifier to discriminate the images into stroke or non-stroke categories following the lesion segmentation.
RESULTS: The mean Dice similarity coefficient (DSC) for the test set was 0.50 ± 0.21 (min-max: 0.07-0.83) and mean net overlap was 0.66 ± 0.18 (min-max: 0.22-1). The experimental results for the classification of strokes from non-strokes showed mean accuracy, precision, sensitivity, and specificity of 73 %, 0.77 %, 84 %, and 69 %, respectively.
COMPARISON WITH EXISTING METHOD: The performance of our methods is comparable with previously published approaches based on machine learning and/or deep learning lesion segmentation techniques. However, most of the previously published methods have yielded low sensitivity, are computationally heavy, and difficult to interpret. The present approach is a significant improvement because it does not require high computation power and memory and can be implemented on a desktop workstation and integrated into the routine clinical diagnostic pipeline.
CONCLUSIONS: The current method is straightforward, fast, and shows good agreement with the lesions identified by human experts.
NEW METHODS: We developed a fully automated method to localize and segment AIS lesions in variable locations for 192 multimodal 3D-magnetic resonance images (MRI) including 106 stroke and 86 healthy cases. The method works based on the Crawford-Howell t-test and comparison of stroke images to healthy controls. We then developed a classifier to discriminate the images into stroke or non-stroke categories following the lesion segmentation.
RESULTS: The mean Dice similarity coefficient (DSC) for the test set was 0.50 ± 0.21 (min-max: 0.07-0.83) and mean net overlap was 0.66 ± 0.18 (min-max: 0.22-1). The experimental results for the classification of strokes from non-strokes showed mean accuracy, precision, sensitivity, and specificity of 73 %, 0.77 %, 84 %, and 69 %, respectively.
COMPARISON WITH EXISTING METHOD: The performance of our methods is comparable with previously published approaches based on machine learning and/or deep learning lesion segmentation techniques. However, most of the previously published methods have yielded low sensitivity, are computationally heavy, and difficult to interpret. The present approach is a significant improvement because it does not require high computation power and memory and can be implemented on a desktop workstation and integrated into the routine clinical diagnostic pipeline.
CONCLUSIONS: The current method is straightforward, fast, and shows good agreement with the lesions identified by human experts.