Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.
Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. Howev...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2075 - 2088 |
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| Autores principales: | , , , , , , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=171950865&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950865 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950865 164410880 171950865 171950865 10.1007/s10278-023-00860-7 171950865 ppf: 2075 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Brain Tumor Segmentation for Multi-Modal MRI with Missing Information. aug: au: Feng, Xue Ghimire, Kanchan Kim, Daniel D. Chandra, Rajat S. Zhang, Helen Peng, Jian Han, Binghong Huang, Gaofeng Chen, Quan Patel, Sohil Bettagowda, Chetan Sair, Haris I. Jones, Craig Jiao, Zhicheng Yang, Li Bai, Harrison affil: https://ror.org/0153tk833 Biomedical Engineering, University of Virginia, 22903, Charlottesville, VA, USA sug: subj: Brain Neoplasms Diagnosis Magnetic Resonance Imaging Neoplasm Staging Human Brain Neoplasms Classification Brain Neoplasms Physiopathology ab: Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge. While one solution is to train multiple models with different MRI sequence combinations, it is impractical to train every model from all possible sequence combinations. In this paper, we propose a DCNN-based brain tumor segmentation framework incorporating a novel sequence dropout technique in which networks are trained to be robust to missing MRI sequences while employing all other available sequences. Experiments were performed on the RSNA-ASNR-MICCAI BraTS 2021 Challenge dataset. When all MRI sequences were available, there were no significant differences in performance of the model with and without dropout for enhanced tumor (ET), tumor (TC), and whole tumor (WT) (p-values 1.000, 1.000, 0.799, respectively), demonstrating that the addition of dropout improves robustness without hindering overall performance. When key sequences were unavailable, the network with sequence dropout performed significantly better. For example, when tested on only T1, T2, and FLAIR sequences together, DSC for ET, TC, and WT increased from 0.143 to 0.486, 0.431 to 0.680, and 0.854 to 0.901, respectively. Sequence dropout represents a relatively simple yet effective approach for brain tumor segmentation with missing MRI sequences. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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