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



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.


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.


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.


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.

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The first author would like to acknowledge Council of Scientific and Industrial Research (CSIR) Senior Research Fellowship grant (No: 9/81(1296)/17) for financial support.

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Correspondence to Nirmalya Ghosh.

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All procedures performed in studies involving human participants were in accordance with ethical standard of the institutional and or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all the participants included in this study.

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Nag, M.K., Chatterjee, S., Sadhu, A.K. et al. Computer-assisted delineation of hematoma from CT volume using autoencoder and Chan Vese model. Int J CARS 14, 259–269 (2019).

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  • Hematoma
  • Computed tomography
  • Autoencoders
  • Chan–Vese model