Automatic detection and segmentation of ischemic lesions in computed tomography images of stroke patients

Stroke is the third most common cause of death in developed countries. Clinical trials are currently investigating whether advanced Computed Tomography can be of benefit for diagnosing stroke at the acute phase. These trials are based on large patients cohorts that need to be manually annotated to obtain a reference standard of tissue loss at follow-up, resulting in extensive workload for the radiologists. Therefore, there is a demand for accurate and reliable automatic lesion segmentation methods. This paper presents a novel method for the automatic detection and segmentation of ischemic lesions in CT images. The method consists of multiple sequential stages. In the initial stage, pixel classification is performed using a naive Bayes classifier in combination with a tissue homogeneity algorithm in order to localize ischemic lesion candidates. In the next stage, the candidates are segmented using a marching cubes algorithm. Regional statistical analysis is used to extract features based on local information as well as contextual information from the contra-lateral hemisphere. Finally, the extracted features are summarized into a likelihood of ischemia by a supervised classifier. An area under the Receiver Operating Characteristic curve of 0.91 was obtained for the identification of ischemic lesions. The method performance on lesion segmentation reached a Dice similarity coeficient (DSC) of 0.74±0.09, whereas an independent human observer obtained a DSC of 0.79±0.11 in the same dataset. The experiments showed that it is feasible to automatically detect and segment ischemic lesions in CT images, obtaining a comparable performance as human observers.