A Deep Graph Cut Model for 3D Brain Tumor Segmentation
Conference paper
De, A., Tiwari, M., Grisan, E. and Chowdhury, A.S. (2022). A Deep Graph Cut Model for 3D Brain Tumor Segmentation. 44th International Engineering in Medicine and Biology Conference (EMBC 2022). Glasgow (UK) 11 - 15 Jul 2022 Institute of Electrical and Electronics Engineers (IEEE).
| Authors | De, A., Tiwari, M., Grisan, E. and Chowdhury, A.S. |
|---|---|
| Type | Conference paper |
| Abstract | Brain tumor segmentation plays a key role in tumor diagnosis and surgical planning. In this paper, we propose a solution to the 3D brain tumor segmentation problem using deep learning and graph cut from the MRI data. In particular, the probability maps of a voxel to belong to the object (tumor) and background classes from the UNet are used to improve the energy function of the graph cut. We derive new expressions for the data term, the region term and the weight factor balancing the data term and the region term for individual voxels in our proposed model. We validate the performance of our model on the publicly available BRATS 2018 dataset. Our segmentation accuracy with a dice similarity score of 0.92 is found to be higher than that of the graph cut and the UNet applied in isolation as well as over a number of state of the art approaches. |
| Year | 2022 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| Web address (URL) | https://embc.embs.org/2022/ |
| Accepted author manuscript | License File Access Level Open |
| Publication dates | |
| 15 Jul 2022 | |
| Publication process dates | |
| Accepted | 04 Apr 2022 |
| Deposited | 29 Apr 2022 |
| Additional information | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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Fiocco, U, Stramare, R, Martini, V, Coran, A, Caso, F, Costa, L, Felicetti, M, Rizzo, G, Tonietto, M, Scanu, A, Oliviero, F, Raffeiner, B, Vezzù, M, Lunardi, F, Scarpa, R, Sacerdoti, D, Rubaltelli, L, Punzi, L, Doria, A and Grisan, E (2017). Quantitative imaging by pixel-based contrast-enhanced ultrasound reveals a linear relationship between synovial vascular perfusion and the recruitment of pathogenic IL-17A-F+IL-23+ CD161+ CD4+ T helper cells in psoriatic arthritis joints. Clinical Rheumatology. 36 (2), pp. 391-399. https://doi.org/10.1007/s10067-016-3500-xBoosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals
Hooshmand, M, Zordan, D, Del Testa, D, Grisan, E and Rossi, M (2017). Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals. IEEE Internet of Things Journal. 4, pp. 1647-1662. https://doi.org/10.1109/JIOT.2017.2689164Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis
Rizzo, G, Tonietto, M, Castellaro, M, Raffeiner, B, Coran, A, Fiocco, U, Stramare, R and Grisan, E (2017). Detection of a slow-flow component in contrast-enhanced ultrasound of the synovia for the differential diagnosis of arthritis. SPIE Medical Imaging. Orlando, FL, USA 11 - 16 Feb 2017 SPIE. https://doi.org/10.1117/12.2250818Improving the quantification of contrast enhanced ultrasound using a Bayesian approach
Rizzo, G, Tonietto, M, Castellaro, M, Raffeiner, B, Coran, A, Fiocco, U, Stramare, R and Grisan, E (2017). Improving the quantification of contrast enhanced ultrasound using a Bayesian approach. SPIE Medical Imaging. Orlando, FL , USA 16 2016 - 11 Feb 2017 SPIE. https://doi.org/10.1117/12.2250195Superpixel-based classification of gastric chromoendoscopy images
Boschetto, D and Grisan, E (2017). Superpixel-based classification of gastric chromoendoscopy images. SPIE Medical Imaging. Orlando, FL, USA 11 - 16 Feb 2017 SPIE. https://doi.org/10.1117/12.2254187Boosted learned kernels for data-driven vesselness measure
Grisan, E (2017). Boosted learned kernels for data-driven vesselness measure. Proceedings Volume 10137, Medical Imaging 2017: Biomedical Applications in Molecular, Structural, and Functional Imaging; 101370Z (2017). Orlando, FL, USA 11 - 16 Feb 2017 SPIE. https://doi.org/10.1117/12.2250370Cortical Thickness variability in Multiple Sclerosis: The role of lesion segmentation and filling
Palombit, A, Castellaro, M, Calabrese, M, Romualdi, C, Pizzini, FB, Montemezzi, S, Grisan, E and Bertoldo, A (2017). Cortical Thickness variability in Multiple Sclerosis: The role of lesion segmentation and filling. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia 18 - 21 Apr 2017 pp. 792-795 https://doi.org/10.1109/ISBI.2017.7950637From macro to nano: Linking quantitative CEUS perfusion parameters to CD4+ T cells subtypes in spondyloarthtitis
Grisan, E, Rizzo, G, Tonietto, M, Coran, A, Raffeiner, B, Scanu, A, Martini, V, Stramare, R and Fiocco, U (2017). From macro to nano: Linking quantitative CEUS perfusion parameters to CD4+ T cells subtypes in spondyloarthtitis. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). Melbourne, VIC, Australia 17 - 21 Apr 2017 Institute of Electrical and Electronics Engineers (IEEE). pp. 899-902 https://doi.org/10.1109/ISBI.2017.7950661Grade and location of power doppler are predictive of damage progression in rheumatoid arthritis patients in clinical remission by anti-tumour necrosis factor α
Raffeiner, B, Grisan, E, Botsios, C, Stramare, R, Rizzo, G, Bernardi, L, Punzi, L, Ometto, F and Doria, A (2017). Grade and location of power doppler are predictive of damage progression in rheumatoid arthritis patients in clinical remission by anti-tumour necrosis factor α. Rheumatology (United Kingdom). 56, pp. 1320-1325. https://doi.org/10.1093/rheumatology/kex084