Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario.
Purpose: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-d...
| Publicado en: | Neuroradiology Vol. 63; no. 8; pp. 1253 - 1263 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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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=151508356&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151508356 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Aug2021 vid: 63 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151508356 148297760 151508356 151508356 10.1007/s00234-021-02649-3 151508356 ppf: 1253 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario. aug: au: Di Ieva, Antonio Russo, Carlo Liu, Sidong Jian, Anne Bai, Michael Y. Qian, Yi Magnussen, John S. affil: Department of Clinical Medicine, Faculty of Medicine, Human and Health Sciences, Macquarie University, Sydney, Australia sug: subj: Brain Neoplasms Diagnosis Magnetic Resonance Imaging Methods Deep Learning Utilization Validity Glioma Human Female Male Artificial Intelligence Neural Networks (Computer) Image Processing, Computer Assisted Brain Neoplasms Radiography Software Descriptive Statistics Female Male ab: Purpose: Accurate brain tumor segmentation on magnetic resonance imaging (MRI) has wide-ranging applications such as radiosurgery planning. Advances in artificial intelligence, especially deep learning (DL), allow development of automatic segmentation that overcome the labor-intensive and operator-dependent manual segmentation. We aimed to evaluate the accuracy of the top-performing DL model from the 2018 Brain Tumor Segmentation (BraTS) challenge, the impact of missing MRI sequences, and whether a model trained on gliomas can accurately segment other brain tumor types. Methods: We trained the model using Medical Decathlon dataset, applied it to the BraTS 2019 glioma dataset, and developed additional models using individual and multimodal MRI sequences. The Dice score was calculated to assess the model's accuracy compared to ground truth labels by neuroradiologists on BraTS dataset. The model was then applied to a local dataset of 105 brain tumors, performance of which was qualitatively evaluated. Results: The DL model using pre- and post-gadolinium contrast T1 and T2 FLAIR sequences performed best, with a Dice score 0.878 for whole tumor, 0.732 tumor core, and 0.699 active tumor. Lack of T1 or T2 sequences did not significantly degrade performance, but FLAIR and T1C were important contributors. All segmentations performed by the model in the local dataset, including non-glioma cases, were considered accurate by a pool of specialists. Conclusion: The DL model could use available MRI sequences to optimize glioma segmentation and adopt transfer learning to segment non-glioma tumors, thereby serving as a useful tool to improve treatment planning and personalized surveillance of patients. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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