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International Journal of Computer Assisted Radiology and Surgery

Published in:

01-02-2019 | Original Article

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

Authors: Manas Kumar Nag, Saunak Chatterjee, Anup Kumar Sadhu, Jyotirmoy Chatterjee, Nirmalya Ghosh

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 2/2019

Abstract

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.

Pustina D, Coslett HB, Turkeltaub PE, Tustison N, Schwartz M, Avants B (2016) Automated segmentation of chronic stroke lesions using LINDA: lesion identification with neighborhood data analysis. Hum Brain Mapp 37:1405–1421. https://doi.org/10.1002/hbm.23110 CrossRefPubMedPubMedCentral

Cheng D-C, Cheng K-S (1998) A PC-based medical image analysis system for brain CT hemorrhage area extraction. In: 11th IEEE symposium on computer-based medical systems. Proceedings. IEEE, pp 240–245

Loncaric S, Dhawan AP, Cosic D, Damagoj K, Joseph B, Thomas B (1999) Quantitative intracerebral brain hemorrhage analysis. In: Medical imaging 1999: image processing. international society for optics and photonics, pp 886–895

Chan T (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph 31:285–298PubMed

Anbeek P, Vincken KL, van Osch MJP, Robertus HCB, Jeroen VG (2004) Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 8:205–215. https://doi.org/10.1016/j.media.2004.06.019 CrossRefPubMed

Seghier ML, Ramlackhansingh A, Crinion J, Leff AP, Price CJ (2008) Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 41:1253–1266PubMed

Liao CC, Xiao F, Wong JM, Chiang IJ (2009) A multiresolution binary level set method and its application to intracranial hematoma segmentation. Comput Med Imaging Graph 33:423–430. https://doi.org/10.1016/j.compmedimag.2009.04.001 CrossRefPubMed

Bardera A, Boada I, Feixas M, Romello S, Blasco G, Silva Y, Pedraza S (2009) Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput Med Imaging Graph 33:304–311. https://doi.org/10.1016/j.compmedimag.2009.02.001 CrossRefPubMed

Liao CC, Xiao F, Wong JM, Chiang IJ (2010) Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography. Comput Med Imaging Graph 34:563–571. https://doi.org/10.1016/j.compmedimag.2010.03.003 CrossRefPubMed

10.

Li Y-H, Zhang L, Hu Q-M, Li H, Jia FC, Wu J (2012) Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. Int J Comput Assist Radiol Surg 7:507–516PubMed

11.

Prakash KNB, Zhou S, Morgan TC, Daniel FH, Wieslaw LN (2012) Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique. Int J Comput Assist Radiol Surg 7:785–798PubMed

12.

Bhadauria HS, Singh A, Dewal ML (2013) An integrated method for hemorrhage segmentation from brain CT Imaging. Comput Electr Eng 39:1527–1536. https://doi.org/10.1016/j.compeleceng.2013.04.010 CrossRef

13.

Gillebert CR, Humphreys GW, Mantini D (2014) Automated delineation of stroke lesions using brain CT images. NeuroImage Clin 4:540–548. https://doi.org/10.1016/j.nicl.2014.03.009 CrossRefPubMedPubMedCentral

14.

Shahangian B, Pourghassem H (2016) Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern Biomed Eng 36:217–232. https://doi.org/10.1016/j.bbe.2015.12.001 CrossRef

15.

De Haan B, Clas P, Juenger H, Wilke M, Karnath HO (2015) Fast semi-automated lesion demarcation in stroke. NeuroImage Clin 9:69–74. https://doi.org/10.1016/j.nicl.2015.06.013 CrossRefPubMedPubMedCentral

16.

Ray S, Kumar V, Ahuja C, Khandelwal N (2017) Intensity population based unsupervised hemorrhage segmentation from brain CT images. Expert Syst Appl 97:325–335

17.

Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging 2018: computer-aided diagnosis. International Society for Optics and Photonics, p 105751C

18.

Gao X, Qian Y (2018) Segmentation of brain lesions from CT images based on deep learning techniques. In: Medical imaging 2018: biomedical applications in molecular, structural, and functional imaging. international society for optics and photonics, p 105782L

19.

Gautam A, Raman B, Raghuvanshi S (2018) A hybrid approach for the delineation of brain lesion from CT images. Biocybern Biomed Eng 38:504–518

20.

Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, pp 281–284

21.

Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277. https://doi.org/10.1109/83.902291 CrossRefPubMed

22.

Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) Fsl Neuroimage 62:782–790PubMed

23.

Pal SK, King RA (1980) Image enhancement using fuzzy set. Electron Lett 16:376–378

24.

Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629–639

25.

Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685

26.

Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–921PubMedPubMedCentral

27.

Kothari RU, Brott T, Broderick JP, Barsan WG, Sauerbeck LR, Zuccarello M, Khoury J (1996) The ABCs of measuring intracerebral hemorrhage volumes. Stroke 27(8):1304–1305PubMed

Title: Computer-assisted delineation of hematoma from CT volume using autoencoder and Chan Vese model
Authors: Manas Kumar Nag
Saunak Chatterjee
Anup Kumar Sadhu
Jyotirmoy Chatterjee
Nirmalya Ghosh
Publication date: 01-02-2019
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 2/2019
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1873-9

At a glance: The STEP trials

Springer Medicine

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

Abstract

Purpose

Methods

Results

Conclusions

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusions

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