A Brain Parenchyma Model-Based Segmentation of Intraventricular and Intracerebral Haemorrhage in CT Scans

Abstract

Accurate quantification of haemorrhage volume in a computed tomography (CT) scan is critical in the management and treatment planning of intraventricular (IVH) and intracerebral haemorrhage (ICH). Manual and semi-automatic methods are laborious and time-consuming limiting their applicability to small data sets. In clinical trials measurements are done at different locations and on a large number of data; an accurate, consistent and automatic method is preferred. A fast and efficient method based on texture energy for identification and segmentation of hemorrhagic regions in the CT scans is proposed. The data set for the study was obtained from CLEAR-IVH clinical trial phase III (41 patients' 201 sequential CT scans from ten different hospitals, slice thickness 2.5-10 mm and from different scanners). The DICOM data were windowed, skull stripped, convolved with textural energy masks and segmented using a hybrid method (a combination of thresholding and fuzzy c-means). Artifacts were removed by statistical analysis and morphological processing. Segmentation results were compared with the ground truth. Descriptive statistics, Dice statistical index (DSI), Bland-Altman and mean difference analysis were carried out. The median sensitivity, specificity and DSI for slice identification and haemorrhage segmentation were 86.25%, 100%, 0.9254 and 84.90%, 99.94%, 0.8710, respectively. The algorithm takes about one minute to process a scan in MATLAB(®). A hybrid method-based volumetry of haemorrhage in CT is reliable, observer independent, efficient, reduces the time and labour. It also generates quantitative data that is important for precise therapeutic decision-making.