Segmentation of brain lesions from CT images based on deep learning techniques

Conference paper

Gao, X. and Qian, Y. 2018. Segmentation of brain lesions from CT images based on deep learning techniques. Gimi, B. and Krol, A. (ed.) SPIE Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging. Houston, Texas, United States 10 - 15 Feb 2018 Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.2286844
TypeConference paper
TitleSegmentation of brain lesions from CT images based on deep learning techniques
AuthorsGao, X. and Qian, Y.
Abstract

While Computerised Tomography (CT) may have been the first clinical tool to study human brains when any suspected abnormality related to the brain occurs, the volumes of CT lesions usually are usually disregarded due to variations among inter-subject measurements. This research responds to this challenge by applying the state of the art deep learning techniques to automatically delineate the boundaries of abnormal features, including tumour, associated edema, head injury, leading to benefiting both patients and clinicians in making timely accurate clinical decisions. The challenge with the application of deep leaning based techniques in medical domain remains that it requires datasets in great abundance, whilst medical data tend to be in small numbers. This work, built on the large field of view of DeepLab convolutional neural network for semantic segmentation, highlights the approaches of both semantics-based and patch-based segmentation to differentiate tumour, lesion and background of the brain. In addition, fusions with a number of other methods to fine tune regional borders are also explored, including conditional random fields (CRF) and multiple scales (MS). With regard to pixel level accuracy, the averaged accuracy rates for segmentation of tumour, lesion and background amount to 82.9%, 85.7%, 85.3% and 81.3% while applying the approaches of DeepLab, DeepLab with MS, DeepLab with MS and CRF, and patch-based pixel-wise classification respectively. In terms of the measurement of intersection over union of two regions, the accuracy rates are of 70.3%, 75.1%, 77.2%, and 63.6% respectively, implying overall DeepLab fused with MS and CRF performs the best.

Keywordsdeep learning; segmentation; classification; CT brain tumours; brain lesions ; DeepLab
ConferenceSPIE Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Proceedings TitleProceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
SeriesProceedings of SPIE
EditorsGimi, B. and Krol, A.
ISSN0277-786X
Electronic1996-756X
ISBN
Hardcover9781510616455
PublisherSociety of Photo-optical Instrumentation Engineers (SPIE)
Publication dates
Print12 Mar 2018
Publication process dates
Deposited03 Nov 2017
Accepted06 Oct 2017
Output statusPublished
Accepted author manuscript
Copyright Statement

Xiaohong Gao, Yu Qian, "Segmentation of brain lesions from CT images based on deep learning techniques", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105782L (12 March 2018); doi: 10.1117/12.2286844; https://doi.org/10.1117/12.2286844
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, or modification of the contents of the publication are prohibited.

Additional information

Article number = 105782L

Digital Object Identifier (DOI)https://doi.org/10.1117/12.2286844
Scopus EID2-s2.0-85049566909
Web of Science identifierWOS:000450869300077
LanguageEnglish
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Gao, X. and Clark, J. 2008. A fast approach to segmentation of PET brain images for extraction of features. Gao, X., Loomes, M., Comley, R., Muller, H. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_25
Prototype system for semantic retrieval of neurological PET images
Batty, S., Clark, J., Fryer, T. and Gao, X. 2008. Prototype system for semantic retrieval of neurological PET images. Gao, X., Muller, H., Loomes, M., Comley, R. and Luo, S. (ed.) International Conference on Medical Imaging and Informatics (MIMI 2007). Beijing, China 14 - 16 Aug 2007 Berlin, Heidelberg Springer. https://doi.org/10.1007/978-3-540-79490-5_23
Colour vision model-based approach for segmentation of traffic signs
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach
Anishenko, S., Osimov, V., Shaposhnikov, D., Podladchikova, L., Comley, R. and Gao, X. 2008. Toward a robust system to monitor the head motions during PET based on facial landmarks detection: a new approach. Puuronen, S., Pechenizkiy, M., Tsymbal, A. and Lee, D. (ed.) 21st IEEE International Symposium on Computer-Based Medical Systems. Jyvaskyla, Finland 17 - 19 Jun 2008 IEEE Computer Society. pp. 50-52 https://doi.org/10.1109/CBMS.2008.19
Detection of head motions using a vision model
Gao, X., Shaposhnikov, D., Podladchikova, L., Batty, S. and Clark, J. 2007. Detection of head motions using a vision model. Bashshur, R. (ed.) 3rd IASTED International Conference on Telehealth. Montreal, QC, Canada 31 May - 01 Jun 2007 Anaheim, CA Acta Press. pp. 167-171
Recognition of traffic signs based on their colour and shape features extracted using human vision models
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Colour management in telemedicine
Gao, X. 2004. Colour management in telemedicine. Hamza, M. (ed.) 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 Anaheim, CA Acta Press. pp. 361-364
Application of vision models to traffic sign recognition
Gao, X., Shaposhnikov, D. and Podladchikova, L. 2004. Application of vision models to traffic sign recognition. Kaynak, O., Alpaydin, E., Oja, E. and Xu, L. (ed.) Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003. Istanbul, Turkey 26 - 29 Sep 2003 Berlin, Heidelberg Springer. https://doi.org/10.1007/3-540-44989-2_131
Towards content-based retrieval for wallpaper images
Qian, Y., Tully, C., Hendon, Z. and Gao, X. 2003. Towards content-based retrieval for wallpaper images. 7th IASTED International Conference on Computer Graphics and Imaging. Kauai, Hawaii, United States 16 - 18 Aug 2004 pp. 305-309
Vision models based identification of traffic signs
Gao, X., Podladchikova, L., Shaposhnikov, D., Shevtsova, N., Hong, K., Batty, S., Golovan, A. and Gusakova, V. 2002. Vision models based identification of traffic signs. 1st European Conference on Colour Graphics, Imaging, and Vision. University of Poitiers, France 02 - 05 Apr 2002 Society for Imaging Science and Technology.
Content based retrieval of lesioned brain images
Batty, S., Blandford, A., Clark, J., Fryer, T. and Gao, X. 2002. Content based retrieval of lesioned brain images. Siegel, E. and Huang, H. (ed.) SPIE Medical Imaging 2002. San Diego, California, United States 23 - 28 Feb 2002 Bellingham Society of Photo-optical Instrumentation Engineers (SPIE). https://doi.org/10.1117/12.466997
A method of vessel tracking for vessel diameter measurement on retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2001. A method of vessel tracking for vessel diameter measurement on retinal images. 2001 International Conference on Image Processing. Thessaloniki, Greece 07 - 10 Oct 2001 IEEE.
Computer algorithms for the automated measurement of retinal arteriolar diameters
Chapman, N., Witt, N., Gao, X., Bharath, A., Stanton, A., Thom, S. and Hughes, A. 2001. Computer algorithms for the automated measurement of retinal arteriolar diameters. British Journal of Ophthalmology. 85 (1), pp. 74-79. https://doi.org/10.1136/bjo.85.1.74
Quantification and characterization of arteries in retinal images
Gao, X., Bharath, A., Stanton, A., Hughes, A., Chapman, N. and Thom, S. 2000. Quantification and characterization of arteries in retinal images. Computer Methods and Programs in Biomedicine. 63 (2), pp. 133-146. https://doi.org/10.1016/S0169-2607(00)00082-1