Computer-assisted delineation of hematoma from CT volume using autoencoder and Chan Vese model

• Published in: International journal for computer assisted radiology and surgery Vol. 14; no. 2; pp. 259 - 269
• Main Authors: Nag, Manas Kumar, Chatterjee, Saunak, Sadhu, Anup Kumar, Chatterjee, Jyotirmoy, Ghosh, Nirmalya
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2019; Springer Nature B.V
• Subjects: Algorithms; Automation; Brain Neoplasms - diagnostic imaging; Computed tomography; Computer Imaging; Computer Science; Computer Simulation; Cone-Beam Computed Tomography - methods; Delineation; Female; Health Informatics; Hematoma - diagnostic imaging; Humans; Image Processing, Computer-Assisted - methods; Imaging; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Radiology; Segmentation; Similarity; Surgery; Vision; Chan–Vese model; Computed tomography; Autoencoders; Hematoma
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-018-1873-9

Purpose To reduce the inter- and intra- rater variability as well as time and effort, a method for computer-assisted delineation of hematoma is proposed. Delineation of hematoma is done for further automated analysis such as the volume of hematoma, anatomical location of hematoma, etc. for proper surgical planning. Methods Fuzzy-based intensifier was used as a pre-processing technique for enhancing the computed tomography (CT) volume. Autoencoder was trained to detect the CT slices with hematoma for initialization. Then active contour Chan–Vese model was used for automated delineation of hematoma from CT volume. Results The proposed algorithm was tested on 48 hemorrhagic patients. Two radiologists have independently segmented the hematoma manually from CT volume. The intersection of two volumes was used as ground-truth for comparison with the segmentation performed by the proposed method. The accuracy was determined by using similarity matrices. The result of sensitivity, positive predictive value, Jaccard index and Dice similarity index were calculated as 0.71 ± 0.12, 0.73 ± 0.18, 0.55 ± 0.14, and 0.70 ± 0.12 respectively. Conclusions A new approach for delineation of hematoma is proposed. The algorithm works well with the whole volume. Similarity indices of the proposed method are comparable with the existing state of art.