Awasthi, N and Pardasani, R and Gupta, S (2021) Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI Scans. In: 6th International MICCAI Brainlesion Workshop, BrainLes 2020, 4 Oct 2020, pp. 168-178.
Full text not available from this repository.Abstract
Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. The segmentation of various regions such as tumor core, enhancing tumor etc. plays an important role in determining severity and prognosis. Here, we have developed a multi-threshold model based on attention U-Net for identification of various regions of the tumor in magnetic resonance imaging (MRI). We propose a multi-path segmentation and built three separate models for the different regions of interest. The proposed model achieved mean Dice Coefficient of 0.59, 0.72, and 0.61 for enhancing tumor, whole tumor and tumor core respectively on the training dataset. The same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation dataset and 0.59, 0.72, and 0.57 on the test dataset. © 2021, Springer Nature Switzerland AG.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Science and Business Media Deutschland GmbH |
| Additional Information: | The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH |
| Keywords: | Brain; Diagnosis; Magnetic resonance imaging; Medical imaging; Statistical tests, Brain tumor segmentation; Brain tumors; Dice coefficient; High grades; Low-grade gliomas; Multithreshold; Regions of interest; Training dataset, Tumors |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 24 Aug 2021 10:52 |
| Last Modified: | 24 Aug 2021 10:52 |
| URI: | http://eprints.iisc.ac.in/id/eprint/69372 |
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