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Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network

arXiv 2203.03338 ↗First posted 7 Mar 2022physics.med-ph cs.CV eess.IVcited by 0

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Abstract (arXiv)

Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation. The objective of this study is to automate the delineation of brain tumors from the FLAIR, T1 weighted, T2 weighted, and T1 weighted contrast-enhanced MR sequences through a deep learning approach, with a focus on determining which MR sequence alone or which combination thereof would lead to the highest accuracy therein.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 7 Mar 2022.

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6 of 31 references.

References · 6

  1. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  2. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  3. 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
  4. 2017NiftyNet: a deep-learning platform for medical imagingcited by 45
  5. 2017Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networkscited by 42
  6. 2017On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Taskcited by 36

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