Investigating certain choices of CNN configurations for brain lesion segmentation

• URL: http://arxiv.org/abs/2212.01235v1
• Date: Fri, 2 Dec 2022 15:24:44 GMT
• Title: Investigating certain choices of CNN configurations for brain lesion segmentation
• Authors: Masoomeh Rahimpour, Ahmed Radwan, Henri Vandermeulen, Stefan Sunaert, Karolien Goffin, Michel Koole
• Abstract summary: Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. We investigated the main design aspects of CNN models for the specific task of MRI-based brain tumor segmentation.
• Score: 5.148195106469231
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Brain tumor imaging has been part of the clinical routine for many years to perform non-invasive detection and grading of tumors. Tumor segmentation is a crucial step for managing primary brain tumors because it allows a volumetric analysis to have a longitudinal follow-up of tumor growth or shrinkage to monitor disease progression and therapy response. In addition, it facilitates further quantitative analysis such as radiomics. Deep learning models, in particular CNNs, have been a methodology of choice in many applications of medical image analysis including brain tumor segmentation. In this study, we investigated the main design aspects of CNN models for the specific task of MRI-based brain tumor segmentation. Two commonly used CNN architectures (i.e. DeepMedic and U-Net) were used to evaluate the impact of the essential parameters such as learning rate, batch size, loss function, and optimizer. The performance of CNN models using different configurations was assessed with the BraTS 2018 dataset to determine the most performant model. Then, the generalization ability of the model was assessed using our in-house dataset. For all experiments, U-Net achieved a higher DSC compared to the DeepMedic. However, the difference was only statistically significant for whole tumor segmentation using FLAIR sequence data and tumor core segmentation using T1w sequence data. Adam and SGD both with the initial learning rate set to 0.001 provided the highest segmentation DSC when training the CNN model using U-Net and DeepMedic architectures, respectively. No significant difference was observed when using different normalization approaches. In terms of loss functions, a weighted combination of soft Dice and cross-entropy loss with the weighting term set to 0.5 resulted in an improved segmentation performance and training stability for both DeepMedic and U-Net models.

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)
• 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)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• 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)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• CNN-based fully automatic wrist cartilage volume quantification in MR Image [55.41644538483948]
  The U-net convolutional neural network with additional attention layers provides the best wrist cartilage segmentation performance. The error of cartilage volume measurement should be assessed independently using a non-MRI method.
  arXiv  Detail & Related papers  (2022-06-22T14:19:06Z)
• Improving Across-Dataset Brain Tissue Segmentation Using Transformer [10.838458766450989]
  This study introduces a novel CNN-Transformer hybrid architecture designed for brain tissue segmentation. We validate our model's performance across four multi-site T1w MRI datasets.
  arXiv  Detail & Related papers  (2022-01-21T15:16:39Z)
• Medulloblastoma Tumor Classification using Deep Transfer Learning with Multi-Scale EfficientNets [63.62764375279861]
  We propose an end-to-end MB tumor classification and explore transfer learning with various input sizes and matching network dimensions. Using a data set with 161 cases, we demonstrate that pre-trained EfficientNets with larger input resolutions lead to significant performance improvements.
  arXiv  Detail & Related papers  (2021-09-10T13:07:11Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Does anatomical contextual information improve 3D U-Net based brain tumor segmentation? [0.0]
  It is investigated whether the addition of contextual information from the brain anatomy improves U-Net-based brain tumor segmentation. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject.
  arXiv  Detail & Related papers  (2020-10-26T09:57:58Z)

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.