Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

• URL: http://arxiv.org/abs/2104.00985v1
• Date: Fri, 2 Apr 2021 11:04:40 GMT
• Title: Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet
• Authors: Mobarakol Islam, Vibashan VS, V Jeya Maria Jose, Navodini Wijethilake, Uppal Utkarsh, Hongliang Ren
• Abstract summary: We develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI) We predict the survival rate using various machine learning methods. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient.
• Score: 11.961432794560103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we develop an attention convolutional neural network (CNN) to segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict the survival rate using various machine learning methods. We adopt a 3D UNet architecture and integrate channel and spatial attention with the decoder network to perform segmentation. For survival prediction, we extract some novel radiomic features based on geometry, location, the shape of the segmented tumor and combine them with clinical information to estimate the survival duration for each patient. We also perform extensive experiments to show the effect of each feature for overall survival (OS) prediction. The experimental results infer that radiomic features such as histogram, location, and shape of the necrosis region and clinical features like age are the most critical parameters to estimate the OS.

Related papers

• Brain Tumor Segmentation Based on Deep Learning, Attention Mechanisms, and Energy-Based Uncertainty Prediction [0.0]
  Brain tumors are one of the deadliest forms of cancer with a mortality rate of over 80%. In medical analysis, the manual annotation and segmentation of a brain tumor can be a complicated task. This paper proposes a region of interest detection algorithm that is implemented during data preprocessing to locate salient features and remove extraneous MRI data. A fully convolutional autoencoder with soft attention segments the different brain MRIs.
  arXiv  Detail & Related papers  (2023-12-31T20:42:52Z)
• Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data [0.0]
  We introduce an ensemble method that comprises eleven unique variations based on three core architectures. Our findings reveal that the ensemble approach, combining different architectures, outperforms single models. These results underline the potential of tailored deep learning techniques in precisely segmenting brain tumors.
  arXiv  Detail & Related papers  (2023-08-14T15:34:22Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning [55.789874096142285]
  We present a deep learning-based brain tumor recurrence location prediction network. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location.
  arXiv  Detail & Related papers  (2023-04-11T02:45:38Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Brain Cancer Survival Prediction on Treatment-na ive MRI using Deep Anchor Attention Learning with Vision Transformer [4.630654643366308]
  Image-based brain cancer prediction models quantify the radiologic phenotype from magnetic resonance imaging (MRI) Despite evidence of intra-tumor phenotypic heterogeneity, the spatial diversity between different slices within an MRI scan has been relatively unexplored using such methods. We propose a deep anchor attention aggregation strategy with a Vision Transformer to predict survival risk for brain cancer patients.
  arXiv  Detail & Related papers  (2022-02-03T21:33:08Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Gray Matter Segmentation in Ultra High Resolution 7 Tesla ex vivo T2w MRI of Human Brain Hemispheres [9.196429840458629]
  We present a high resolution 7 Tesla dataset of 32 ex vivo human brain specimens. We benchmark the cortical mantle segmentation performance of nine neural network architectures. We show excellent generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at different magnetic field strength and imaging sequences.
  arXiv  Detail & Related papers  (2021-10-14T21:01:18Z)
• A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections [46.414990784180546]
  Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique. CNNs require large annotated datasets for training. Self-supervised learning strategies can leverage unlabeled data for training.
  arXiv  Detail & Related papers  (2021-05-24T12:27:06Z)
• MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures [0.0]
  This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS'20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.
  arXiv  Detail & Related papers  (2020-12-30T19:28:53Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.