naa a22 4500 510807054 CHVBK 20180411083423.0 cr unu---uuuuu 180411e20131101xx s 000 0 eng 10.1007/s11548-013-0830-x doi (NATIONALLICENCE)springer-10.1007/s11548-013-0830-x Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning [Elektronische Daten] [Yuanxiu Zhang, Mingyang Chen, Qingmao Hu, Wenhua Huang] Purpose: Hemorrhage within the brain space (HWBS) involves the brain parenchyma and ventricle systems, and is associated with high morbidity and mortality. Computed tomography (CT) head scans are the recommended modality for diagnosis and treatment for HWBS. However, HWBS detection may be difficult when the hemorrhage is inconspicuous, while quantification is hard as hemorrhage can have very variable intensity that overlaps with normal brain tissue. An algorithm is proposed to detect and quantify HWBS. Methods: Adaptive thresholding and case-based reasoning (CBR) were applied to HWBS in four steps: preprocessing to extract the brain, adaptive thresholding based on local contrast with varied window sizes to derive candidate HWBS regions, case representation to represent each candidate HWBS region by parameters on context as well as intensity and geometrical characteristics, and classification of HWBS by taking each candidate HWBS region as a case and applying CBR. Additionally, case base indexing and weights optimization were used to increase retrieval speed and improve performances. Refinement of each recognized HWBS was performed for quantifying HWBS. Results: Validation on 426 clinical CT data indicates that the proposed algorithm achieved a detection rate of 94.4% and recall of 79.2% for detecting HWBS regions. Visually, the HWBS regions calculated from adaptive thresholding plus refinement agreed well with expert delineation. For 10 representative data with small to large hemorrhage, the algorithm quantitatively yielded a segmentation accuracy of $$0.950\pm 0.015$$ 0.950 ± 0.015 . Case base indexing increased the retrieval speed by 41.1 times at the expense of decreasing detection rate of 0.5% and recall of 2.6%. Genetic algorithm optimization enhanced the detection rate and recall to, respectively, 94.9 and 83.5%. Conclusions: We developed and tested an algorithm that combined adaptive thresholding and CBR for detecting and quantifying HWBS. Experiments showed that adaptive thresholding could provide suitable candidates, while CBR was able to identify HWBS regions. The proposed method has potential as a new tool for accurately detecting and quantifying HWBS. CARS, 2013 Hemorrhage within the brain space nationallicence Computed tomography nationallicence Adaptive thresholding nationallicence Case-based reasoning nationallicence Zhang Yuanxiu Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut Chen Mingyang Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut Hu Qingmao Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut Huang Wenhua Nanfang Medical University, Guangzhou, China aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/6(2013-11-01), 917-927 1861-6410 8:6<917 2013 8 11548 https://doi.org/10.1007/s11548-013-0830-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-013-0830-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhang Yuanxiu Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut NATIONALLICENCE 700 1- Chen Mingyang Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut NATIONALLICENCE 700 1- Hu Qingmao Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China aut NATIONALLICENCE 700 1- Huang Wenhua Nanfang Medical University, Guangzhou, China aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/6(2013-11-01), 917-927 1861-6410 8:6<917 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer