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SYNERGISTIC MRI SEGMENTATION OF ISCHEMIC BRAIN INFARCTS FROM DWI AND ADC MAPS
Background and Aims
Manual localization and quantification of silent brain infarcts is costly and challenging for clinicians, especially for studies where an accurate outcome is of enormous importance. The two most important challenges in the automated detection of infarcts, consist in the high class-imbalance and the presence of considerable white matter hyper-intensities, which are often miss-classified by automatic lesion segmentation methods. For that purpose we develop a deep learning framework which is capable of detecting these lesions.
The framework essentially consists of two modules. In the first step, a synergistic multi-scale network (the χ-Net) is applied, which fuses the complementary information contained in DWI and ADC. The network consists of two contracting paths and two up-sampling paths, whereby the information from the two down-sampling processes is fused. The resulting synergistic segmentation masks are further reﬁned by an additional network block reducing the false positive rate by explicitly learning the difference between ischaemia and other hyper-intensity.
Training and validation of the networks was carried out using data from patients who suffered stroke and admitted to our Radiology department. The proposed framework achieves a sensitivity of 0.9755 and a Dice coefficient of 0.8222.
The proposed χ-Net architecture with additional peeling module delivers promising results in stroke segmentation task. In order to be able to make more precise statistical statements about performance, we will carry out the experiments on a larger data set or on other medical imaging problems, where images which are derived from different modalities carry complementary information (e.g. CT and MR perfusion data).