A deep learning model integrating SK-TPCNN and random forests for brain tumor segmentation in MRI

• Autorzy: Yang, Tiejun , Song, Jikun , Li, Lei
• Języki publikacji: EN

Abstrakty

EN

The segmentation of brain tumors in magnetic resonance imaging (MRI) images plays an important role in early diagnosis, treatment planning and outcome evaluation. However, due to gliomas' significant diversity in structure, the segmentation accuracy is low. In this paper, an automatic segmentation method integrating the small kernels two-path convolu-tional neural network (SK-TPCNN) and random forests (RF) is proposed, the feature extrac-tion ability of SK-TPCNN and the joint optimization capability of model are presented respectively. The SK-TPCNN structure combining the small convolutional kernels and large convolutional kernels can enhance the nonlinear mapping ability and avoid over-fitting, the multiformity of features is also increased. The learned features from SK-TPCNN are then applied to the RF classifier to implement the joint optimization. RF classifier effectively integrates redundancy features and classify each MRI image voxel into normal brain tissues and different parts of tumor. The proposed algorithm is validated and evaluated in the Brain Tumor Segmentation Challenge (Brats) 2015 challenge Training dataset and the better performance is achieved.

Słowa kluczowe

PL

rezonans magnetyczny; konwolucyjna sieć neuronowa; lasy losowe; segmentacja guza

EN

magnetic resonance imaging; convolutional neural network; random forests; brain tumor segmentation

• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2019
• Tom: Vol. 39, no. 3
• Strony: 613--623
• Opis fizyczny: Bibliogr. 39 poz., rys., tab., wykr.

Twórcy

autor

Yang, Tiejun

• College of Information Science and Technology, Henan University of Technology, Zhengzhou, Henan, China

autor

Song, Jikun

• College of Information Science and Technology, Henan University of Technology, No.100 LianHua Street, High-Tech Zone, Zhengzhou, Henan, China,

autor

Li, Lei

• College of Information Science and Technology, Henan University of Technology, Zhengzhou, Henan, China

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).

Identyfikatory

DOI

10.1016/j.bbe.2019.06.003