Skip to main content
Top
Published in:

2021 | OriginalPaper | Chapter

Lightweight U-Nets for Brain Tumor Segmentation

Authors : Tomasz Tarasiewicz, Michal Kawulok, Jakub Nalepa

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Automated brain tumor segmentation is a vital topic due to its clinical applications. We propose to exploit a lightweight U-Net-based deep architecture called Skinny for this task—it was originally employed for skin detection from color images, and benefits from a wider spatial context. We train multiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.

To get access to this content you need the following product:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Footnotes
1
Our team is named ttarasiewicz.
 
Literature
4.
go back to reference Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629 (2018). http://arxiv.org/abs/1811.02629 Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629 (2018). http://​arxiv.​org/​abs/​1811.​02629
5.
go back to reference Bauer, S., Seiler, C., Bardyn, T., Buechler, P., Reyes, M.: Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration. In: Proceedings of the IEEE EMBC, pp. 4080–4083 (2010) Bauer, S., Seiler, C., Bardyn, T., Buechler, P., Reyes, M.: Atlas-based segmentation of brain tumor images using a Markov random field-based tumor growth model and non-rigid registration. In: Proceedings of the IEEE EMBC, pp. 4080–4083 (2010)
6.
go back to reference Bontempi, D., Benini, S., Signoroni, A., Svanera, M., Muckli, L.: Cerebrum: a fast and fully-volumetric convolutional encoder-decoder for weakly-supervised segmentation of brain structures from out-of-the-scanner MRI. Med. Image Anal. 62, 101688 (2020)CrossRef Bontempi, D., Benini, S., Signoroni, A., Svanera, M., Muckli, L.: Cerebrum: a fast and fully-volumetric convolutional encoder-decoder for weakly-supervised segmentation of brain structures from out-of-the-scanner MRI. Med. Image Anal. 62, 101688 (2020)CrossRef
7.
go back to reference Bousselham, A., Bouattane, O., Youss, M., Raihani, A.: Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area. Int. J. Biomed. Imaging 2019, 1758948 (2019)CrossRef Bousselham, A., Bouattane, O., Youss, M., Raihani, A.: Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area. Int. J. Biomed. Imaging 2019, 1758948 (2019)CrossRef
8.
go back to reference Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38(5), 4998–5004 (2011)CrossRef Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38(5), 4998–5004 (2011)CrossRef
9.
go back to reference Chowdhary, C.L., Acharjya, D.: Segmentation and feature extraction in medical imaging: a systematic review. Procedia Comput. Sci. 167, 26–36 (2020)CrossRef Chowdhary, C.L., Acharjya, D.: Segmentation and feature extraction in medical imaging: a systematic review. Procedia Comput. Sci. 167, 26–36 (2020)CrossRef
10.
go back to reference Estienne, T., et al.: Deep learning-based concurrent brain registration and tumor segmentation. Front. Comput. Neurosci. 14, 17 (2020)CrossRef Estienne, T., et al.: Deep learning-based concurrent brain registration and tumor segmentation. Front. Comput. Neurosci. 14, 17 (2020)CrossRef
11.
go back to reference Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 57(2), 378–390 (2011)CrossRef Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 57(2), 378–390 (2011)CrossRef
12.
13.
go back to reference Hasan, S.M.K., Ahmad, M.: Two-step verification of brain tumor segmentation using watershed-matching algorithm. Brain Inform. 5(2), 8 (2018)CrossRef Hasan, S.M.K., Ahmad, M.: Two-step verification of brain tumor segmentation using watershed-matching algorithm. Brain Inform. 5(2), 8 (2018)CrossRef
15.
go back to reference Ji, S., Wei, B., Yu, Z., Yang, G., Yin, Y.: A new multistage medical segmentation method based on superpixel and fuzzy clustering. Comput. Math. Methods Med. 2014, 747549:1–747549:13 (2014)MathSciNetCrossRef Ji, S., Wei, B., Yu, Z., Yang, G., Yin, Y.: A new multistage medical segmentation method based on superpixel and fuzzy clustering. Comput. Math. Methods Med. 2014, 747549:1–747549:13 (2014)MathSciNetCrossRef
17.
go back to reference Korfiatis, P., Kline, T.L., Erickson, B.J.: Automated segmentation of hyperintense regions in FLAIR MRI using deep learning. Tomogr. J. Imaging Res. 2(4), 334–340 (2016) Korfiatis, P., Kline, T.L., Erickson, B.J.: Automated segmentation of hyperintense regions in FLAIR MRI using deep learning. Tomogr. J. Imaging Res. 2(4), 334–340 (2016)
18.
go back to reference Ladgham, A., Torkhani, G., Sakly, A., Mtibaa, A.: Modified support vector machines for MR brain images recognition. In: Proceedings of the CoDIT, pp. 032–035 (2013) Ladgham, A., Torkhani, G., Sakly, A., Mtibaa, A.: Modified support vector machines for MR brain images recognition. In: Proceedings of the CoDIT, pp. 032–035 (2013)
19.
go back to reference Meier, R., et al.: Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci. Rep. 6(1), 23376 (2016)CrossRef Meier, R., et al.: Clinical evaluation of a fully-automatic segmentation method for longitudinal brain tumor volumetry. Sci. Rep. 6(1), 23376 (2016)CrossRef
20.
go back to reference Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef
21.
go back to reference Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Isgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRef Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Isgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)CrossRef
24.
go back to reference Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain-tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)CrossRef Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain-tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)CrossRef
25.
go back to reference Nalepa, J., et al.: Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif. Intell. Med. 102, 101769 (2020)CrossRef Nalepa, J., et al.: Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif. Intell. Med. 102, 101769 (2020)CrossRef
26.
go back to reference Ortiz, A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D.: MRI brain image segmentation with supervised SOM and probability-based clustering method. In: Proceedings of the IWINAC, pp. 49–58 (2011) Ortiz, A., Górriz, J.M., Ramírez, J., Salas-Gonzalez, D.: MRI brain image segmentation with supervised SOM and probability-based clustering method. In: Proceedings of the IWINAC, pp. 49–58 (2011)
27.
go back to reference Ouchicha, C., Ammor, O., Meknassi, M.: Unsupervised brain tumor segmentation from magnetic resonance images. In: Proceedings of the IEEE WINCOM, pp. 1–5 (2019) Ouchicha, C., Ammor, O., Meknassi, M.: Unsupervised brain tumor segmentation from magnetic resonance images. In: Proceedings of the IEEE WINCOM, pp. 1–5 (2019)
28.
go back to reference Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M.L.D., Silva, C.A.: Brain tumour segmentation based on extremely randomized forest with high-level features. In: Proceedings of the IEEE EMBC, pp. 3037–3040 (2015) Pinto, A., Pereira, S., Correia, H., Oliveira, J., Rasteiro, D.M.L.D., Silva, C.A.: Brain tumour segmentation based on extremely randomized forest with high-level features. In: Proceedings of the IEEE EMBC, pp. 3037–3040 (2015)
29.
go back to reference Pipitone, J., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014)CrossRef Pipitone, J., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014)CrossRef
30.
go back to reference Saha, S., Bandyopadhyay, S.: MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. In: Proceedings of the IEEE CEC, pp. 4417–4424 (2007) Saha, S., Bandyopadhyay, S.: MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. In: Proceedings of the IEEE CEC, pp. 4417–4424 (2007)
31.
go back to reference Simi, V., Joseph, J.: Segmentation of glioblastoma multiforme from MR images - a comprehensive review. Egypt. J. Radiol. Nuclear Med. 46(4), 1105–1110 (2015)CrossRef Simi, V., Joseph, J.: Segmentation of glioblastoma multiforme from MR images - a comprehensive review. Egypt. J. Radiol. Nuclear Med. 46(4), 1105–1110 (2015)CrossRef
32.
go back to reference Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810 (2019)CrossRef Sun, L., Zhang, S., Chen, H., Luo, L.: Brain tumor segmentation and survival prediction using multimodal MRI scans with deep learning. Front. Neurosci. 13, 810 (2019)CrossRef
33.
go back to reference Tarasiewicz, T., Nalepa, J., Kawulok, M.: Skinny: a lightweight U-Net for skin detection and segmentation. In: Proceedings of the IEEE ICIP, pp. 2386–2390 (2020) Tarasiewicz, T., Nalepa, J., Kawulok, M.: Skinny: a lightweight U-Net for skin detection and segmentation. In: Proceedings of the IEEE ICIP, pp. 2386–2390 (2020)
34.
go back to reference Verma, N., Cowperthwaite, M.C., Markey, M.K.: Superpixels in brain MR image analysis. In: Proceedings of the IEEE EMBC, pp. 1077–1080 (2013) Verma, N., Cowperthwaite, M.C., Markey, M.K.: Superpixels in brain MR image analysis. In: Proceedings of the IEEE EMBC, pp. 1077–1080 (2013)
35.
go back to reference Wu, W., Chen, A.Y.C., Zhao, L., Corso, J.J.: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. 9(2), 241–253 (2014)CrossRef Wu, W., Chen, A.Y.C., Zhao, L., Corso, J.J.: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. 9(2), 241–253 (2014)CrossRef
36.
go back to reference Zhuge, Y., et al.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234–5243 (2017)CrossRef Zhuge, Y., et al.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234–5243 (2017)CrossRef
Metadata
Title
Lightweight U-Nets for Brain Tumor Segmentation
Authors
Tomasz Tarasiewicz
Michal Kawulok
Jakub Nalepa
Copyright Year
2021
DOI
https://doi.org/10.1007/978-3-030-72087-2_1

Premium Partner