Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans

• URL: http://arxiv.org/abs/2101.12404v1
• Date: Fri, 29 Jan 2021 04:53:42 GMT
• Title: Multi-Threshold Attention U-Net (MTAU) based Model for Multimodal Brain Tumor Segmentation in MRI scans
• Authors: Navchetan Awasthi, Rohit Pardasani and Swati Gupta
• Abstract summary: Gliomas are one of the most frequent brain tumors and are classified into high grade and low grade gliomas. 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) 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.
• Score: 5.586191108738564
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images [62.53931644063323]
  In this study we extended the capabilities of TotalSegmentator to MR images. We trained an nnU-Net segmentation algorithm on this dataset and calculated similarity coefficients (Dice) to evaluate the model's performance. The model significantly outperformed two other publicly available segmentation models (Dice score 0.824 versus 0.762; p0.001 and 0.762 versus 0.542; p)
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• Cross-modality Guidance-aided Multi-modal Learning with Dual Attention for MRI Brain Tumor Grading [47.50733518140625]
  Brain tumor represents one of the most fatal cancers around the world, and is very common in children and the elderly. We propose a novel cross-modality guidance-aided multi-modal learning with dual attention for addressing the task of MRI brain tumor grading.
  arXiv  Detail & Related papers  (2024-01-17T07:54:49Z)
• Glioblastoma Tumor Segmentation using an Ensemble of Vision Transformers [0.0]
  Glioblastoma is one of the most aggressive and deadliest types of brain cancer. Brain Radiology Aided by Intelligent Neural NETworks (BRAINNET) generates robust tumor segmentation maks.
  arXiv  Detail & Related papers  (2023-11-09T18:55:27Z)
• 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)
• Automated ensemble method for pediatric brain tumor segmentation [0.0]
  This study introduces a novel ensemble approach using ONet and modified versions of UNet. Data augmentation ensures robustness and accuracy across different scanning protocols. Results indicate that this advanced ensemble approach offers promising prospects for enhanced diagnostic accuracy.
  arXiv  Detail & Related papers  (2023-08-14T15:29:32Z)
• Multi-class Brain Tumor Segmentation using Graph Attention Network [3.3635982995145994]
  This work introduces an efficient brain tumor summation model by exploiting the advancement in MRI and graph neural networks (GNNs) The model represents the volumetric MRI as a region adjacency graph (RAG) and learns to identify the type of tumors through a graph attention network (GAT)
  arXiv  Detail & Related papers  (2023-02-11T04:30:40Z)
• 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)
• H2NF-Net for Brain Tumor Segmentation using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task [96.49879910148854]
  Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions. We trained and evaluated our model on the Multimodal Brain Tumor Challenge (BraTS) 2020 dataset. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.
  arXiv  Detail & Related papers  (2020-12-30T20:44:55Z)
• Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning [3.2803205051531235]
  We present a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 $pm$ 1.5% of Dice score and the segmentation of tumors alone reached 61.8 $pm$ 4.0% Dice score.
  arXiv  Detail & Related papers  (2020-12-23T17:31:08Z)
• Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution [56.17099252139182]
  We automate and standardize the task of brain tumor segmentation with U-net like neural networks. Two independent ensembles of models were trained, and each produced a brain tumor segmentation map. Our solution achieved a Dice of 0.79, 0.89 and 0.84, as well as Hausdorff 95% of 20.4, 6.7 and 19.5mm on the final test dataset.
  arXiv  Detail & Related papers  (2020-10-30T14:36:10Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.