Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand

• URL: http://arxiv.org/abs/2207.07622v1
• Date: Fri, 15 Jul 2022 17:34:05 GMT
• Title: Brain MRI study for glioma segmentation using convolutional neural networks and original post-processing techniques with low computational demand
• Authors: Jos\'e Gerardo Su\'arez-Garc\'ia Javier Miguel Hern\'andez-L\'opez, Eduardo Moreno-Barbosa, and Benito de Celis-Alonso
• Abstract summary: Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed.
• Score: 0.6719751155411076
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Gliomas are brain tumors composed of different highly heterogeneous histological subregions. Image analysis techniques to identify relevant tumor substructures have high potential for improving patient diagnosis, treatment and prognosis. However, due to the high heterogeneity of gliomas, the segmentation task is currently a major challenge in the field of medical image analysis. In the present work, the database of the Brain Tumor Segmentation (BraTS) Challenge 2018, composed of multimodal MRI scans of gliomas, was studied. A segmentation methodology based on the design and application of convolutional neural networks (CNNs) combined with original post-processing techniques with low computational demand was proposed. The post-processing techniques were the main responsible for the results obtained in the segmentations. The segmented regions were the whole tumor, the tumor core, and the enhancing tumor core, obtaining averaged Dice coefficients equal to 0.8934, 0.8376, and 0.8113, respectively. These results reached the state of the art in glioma segmentation determined by the winners of the challenge.

Related papers

• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.586530244472655]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI [2.9746083684997418]
  This study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. A Convolutional Neural Network (CNN) architecture is employed for tumor classification. 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach.
  arXiv  Detail & Related papers  (2024-05-07T05:55:50Z)
• Fully Automated Tumor Segmentation for Brain MRI data using Multiplanner UNet [0.29998889086656577]
  This study evaluates the efficacy of the Multi-Planner U-Net (MPUnet) approach in segmenting different tumor subregions across three challenging datasets.
  arXiv  Detail & Related papers  (2024-01-12T10:46:19Z)
• 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)
• Cross-Modality Deep Feature Learning for Brain Tumor Segmentation [158.8192041981564]
  This paper proposes a novel cross-modality deep feature learning framework to segment brain tumors from the multi-modality MRI data. The core idea is to mine rich patterns across the multi-modality data to make up for the insufficient data scale. Comprehensive experiments are conducted on the BraTS benchmarks, which show that the proposed cross-modality deep feature learning framework can effectively improve the brain tumor segmentation performance.
  arXiv  Detail & Related papers  (2022-01-07T07:46:01Z)
• Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI [0.0]
  We propose a new aggregation of two deep learning frameworks namely, DeepSeg and nnU-Net for automatic glioblastoma recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions.
  arXiv  Detail & Related papers  (2021-12-13T10:51:20Z)
• 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)
• Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI [47.26574993639482]
  We show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales.
  arXiv  Detail & Related papers  (2020-06-23T09:20:42Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• Region of Interest Identification for Brain Tumors in Magnetic Resonance Images [8.75217589103206]
  We propose a fast, automated method, with light computational complexity, to find the smallest bounding box around the tumor region. This region-of-interest can be used as a preprocessing step in training networks for subregion tumor segmentation. The proposed method is evaluated on the BraTS 2015 dataset, and the average gained DICE score is 0.73, which is an acceptable result for this application.
  arXiv  Detail & Related papers  (2020-02-26T14:10:40Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)

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.