Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
- URL: http://arxiv.org/abs/2001.02040v1
- Date: Mon, 6 Jan 2020 07:47:42 GMT
- Title: Robust Semantic Segmentation of Brain Tumor Regions from 3D MRIs
- Authors: Andriy Myronenko and Ali Hatamizadeh
- Abstract summary: Multimodal brain tumor segmentation challenge (BraTS) brings together researchers to improve automated methods for 3D MRI brain tumor segmentation.
We evaluate the method on BraTS 2019 challenge.
- Score: 2.4736005621421686
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Multimodal brain tumor segmentation challenge (BraTS) brings together
researchers to improve automated methods for 3D MRI brain tumor segmentation.
Tumor segmentation is one of the fundamental vision tasks necessary for
diagnosis and treatment planning of the disease. Previous years winning methods
were all deep-learning based, thanks to the advent of modern GPUs, which allow
fast optimization of deep convolutional neural network architectures. In this
work, we explore best practices of 3D semantic segmentation, including
conventional encoder-decoder architecture, as well combined loss functions, in
attempt to further improve the segmentation accuracy. We evaluate the method on
BraTS 2019 challenge.
Related papers
- MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network [0.0]
This study proposes the MBDRes-U-Net model using the three-dimensional (3D) U-Net framework, which integrates multibranch residual blocks and fused attention into the model.
The computational burden of the model is reduced by the branch strategy, which effectively uses the rich local features in multimodal images.
arXiv Detail & Related papers (2024-11-04T09:03:43Z) - Enhancing Weakly Supervised 3D Medical Image Segmentation through
Probabilistic-aware Learning [52.249748801637196]
3D medical image segmentation is a challenging task with crucial implications for disease diagnosis and treatment planning.
Recent advances in deep learning have significantly enhanced fully supervised medical image segmentation.
We propose a novel probabilistic-aware weakly supervised learning pipeline, specifically designed for 3D medical imaging.
arXiv Detail & Related papers (2024-03-05T00:46:53Z) - An Optimization Framework for Processing and Transfer Learning for the
Brain Tumor Segmentation [2.0886519175557368]
We have constructed an optimization framework based on a 3D U-Net model for brain tumor segmentation.
This framework incorporates a range of techniques, including various pre-processing and post-processing techniques, and transfer learning.
On the validation datasets, this multi-modality brain tumor segmentation framework achieves an average lesion-wise Dice score of 0.79, 0.72, 0.74 on Challenges 1, 2, 3 respectively.
arXiv Detail & Related papers (2024-02-10T18:03:15Z) - 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) - 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) - 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) - 3-Dimensional Deep Learning with Spatial Erasing for Unsupervised
Anomaly Segmentation in Brain MRI [55.97060983868787]
We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance.
We compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance.
Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE.
arXiv Detail & Related papers (2021-09-14T09:17:27Z) - Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification.
We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
arXiv Detail & Related papers (2021-08-08T11:26:34Z) - QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors [0.0]
Manual segmentation of brain tumors from 3D MRI volumes is a time-consuming task.
Our model, QuickTumorNet, demonstrated fast, reliable, and accurate brain tumor segmentation.
arXiv Detail & Related papers (2020-12-22T23:16:43Z) - HI-Net: Hyperdense Inception 3D UNet for Brain Tumor Segmentation [17.756591105686]
This paper proposes hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block.
Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively.
arXiv Detail & Related papers (2020-12-12T09:09:04Z) - 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation [49.32653090178743]
We investigate whether extending this problem to full 4D deep learning using a history of MRI volumes can improve performance.
We find that our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
arXiv Detail & Related papers (2020-04-20T11:41:01Z)
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.