Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique

Abstract

Purpose An automatic, accurate and fast segmentation of hemorrhage in brain Computed Tomography (CT) images is necessary for quantification and treatment planning when assessing a large number of data sets. Though manual segmentation is accurate, it is time consuming and tedious. Semi-automatic methods need user interactions and might introduce variability in results. Our study proposes a modified distance regularized level set evolution (MDRLSE) algorithm for hemorrhage segmentation. Methods Study data set (from the ongoing CLEAR-IVH phase III clinical trial) is comprised of 200 sequential CT scans of 40 patients collected at 10 different hospitals using different machines/vendors. Data set contained both constant and variable slice thickness scans. Our study included pre-processing (filtering and skull removal), segmentation (MDRLSE which is a two-stage method with shrinking and expansion) with modified parameters for faster convergence and higher accuracy and post-processing (reduction in false positives and false negatives). Results Results are validated against the gold standard marked manually by a trained CT reader and neurologist. Data sets are grouped as small, medium and large based on the volume of blood. Statistical analysis is performed for both training and test data sets in each group. The median Dice statistical indices (DSI) for the 3 groups are 0.8971, 0.8580 and 0.9173 respectively. Pre- and post-processing enhanced the DSI by 8 and 4% respectively. Conclusions The MDRLSE improved the accuracy and speed for segmentation and calculation of the hemorrhage volume compared to the original DRLSE method. The method generates quantitative information, which is useful for specific decision making and reduces the time needed for the clinicians to localize and segment the hemorrhagic regions.

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

“…CCCs for algorithms based on clustering, graph theory, and modified thresholding were 0.87, 0.91, and 0.97, respectively, and they are comparable to the CCCs calculated for our segmentation algorithm. 10,[29][30][31] This supports our approach to combine multiple segmentation strategies within a random forest model to tackle challenging segmentation tasks and is a promising distinction of our algorithm.…”

Intracranial Hemorrhage Segmentation Using a Deep Convolutional Model

“…Some researchers have extended the scope and performed the ICH segmentation to identify the region of ICH [7,11,15,17,[19][20][21][22][23][24][25][26]. Most researchers validated their algorithms using small datasets [7][8][9][10][11][12][13]17,[20][21][22][24][25][26], while a few used large datasets for testing and validating [6,[14][15][16]18,19,23]. We provide a comprehensive review of the published papers for the ICH detection and segmentation ( Figure 1) in this section.…”

“…It is essential to localize and find the ICH volume to decide on the appropriate medical and surgical intervention [27]. Several methods were proposed to automate the process of the ICH segmentation [7,11,15,17,[19][20][21][22][23][24][25][26]. Similar to the ICH detection, the ICH delineation approaches can be divided into traditional [7,11,[20][21][22]26] and deep learning methods [15,17,19,[23][24][25].…”

Weighted Level Set Evolution Based on Local Edge Features for Medical Image Segmentation

“…Term R(φ) is employed to maintain a desired shape of the LSF, as it has been previously shown that the LSF usually becomes too flat or too steep near the zero level set, resulting in numerical errors which may eventually affect the stability of the evolution [13], [32], [50], [51]. Term Length(φ) is related to the energy along the length of the evolving contour C, i.e., for the case where φ =0; while term Area(φ) is related to the energy of the area inside of C, i.e., for the case where φ >=0.…”