Efficient embedding network for 3D brain tumor segmentation

• URL: http://arxiv.org/abs/2011.11052v1
• Date: Sun, 22 Nov 2020 16:17:29 GMT
• Title: Efficient embedding network for 3D brain tumor segmentation
• Authors: Hicham Messaoudi, Ahror Belaid, Mohamed Lamine Allaoui, Ahcene Zetout, Mohand Said Allili, Souhil Tliba, Douraied Ben Salem, Pierre-Henri Conze
• Abstract summary: In this paper, we investigate a way to transfer the performance of a two-dimensional classiffication network for the purpose of three-dimensional semantic segmentation of brain tumors. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance.
• Score: 0.33727511459109777
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and trained. In this paper, we investigate a way to transfer the performance of a two-dimensional classiffication network for the purpose of three-dimensional semantic segmentation of brain tumors. We propose an asymmetric U-Net network by incorporating the EfficientNet model as part of the encoding branch. As the input data is in 3D, the first layers of the encoder are devoted to the reduction of the third dimension in order to fit the input of the EfficientNet network. Experimental results on validation and test data from the BraTS 2020 challenge demonstrate that the proposed method achieve promising performance.

Related papers

• E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation [36.367368163120794]
  We propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet) It incorporates two parametrically and computationally efficient designs. It consistently achieves a superior trade-off between accuracy and efficiency across various resource constraints.
  arXiv  Detail & Related papers  (2023-12-07T22:13:37Z)
• Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation [4.5206601127476445]
  We propose a novel convolutional neural network (CNN) and self-supervised learning (SSL) method for label-efficient 3D-to-2D segmentation. Results on different datasets demonstrate that the proposed CNN significantly improves the state of the art in scenarios with limited labeled data by up to 8% in Dice score.
  arXiv  Detail & Related papers  (2023-07-06T14:16:25Z)
• NeRF-GAN Distillation for Efficient 3D-Aware Generation with Convolutions [97.27105725738016]
  integration of Neural Radiance Fields (NeRFs) and generative models, such as Generative Adversarial Networks (GANs) has transformed 3D-aware generation from single-view images. We propose a simple and effective method, based on re-using the well-disentangled latent space of a pre-trained NeRF-GAN in a pose-conditioned convolutional network to directly generate 3D-consistent images corresponding to the underlying 3D representations.
  arXiv  Detail & Related papers  (2023-03-22T18:59:48Z)
• OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data [21.42609249273068]
  Convolutional neural networks (CNNs) are the current state-of-the-art meta-algorithm for volumetric segmentation of medical data. A key limitation of 3D CNNs on voxelised data is that the memory consumption grows cubically with the training data resolution. We propose Occupancy Networks (OSS-Nets) to accurately and memory-efficiently segment 3D medical data.
  arXiv  Detail & Related papers  (2021-10-20T16:14:26Z)
• Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation [4.150096314396549]
  Deep convolutional neural network (DCNNs) has obtained tremendous success in 2D and 3D medical image segmentation. We propose a novel dense-sparse training flow from a data perspective, in which, densely adjacent slices and sparsely adjacent slices are extracted as inputs for regularizing DCNNs. We also design a 2.5D light-weight nnU-Net from a network perspective, in which, depthwise separable convolutions are adopted to improve the efficiency.
  arXiv  Detail & Related papers  (2021-08-15T15:29:48Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation [141.2690520327948]
  We propose a two-stream graph convolutional network (TSGCNet) to learn multi-view information from different geometric attributes. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners.
  arXiv  Detail & Related papers  (2020-12-26T08:02:56Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-fine Framework and Its Adversarial Examples [74.92488215859991]
  We propose a novel 3D-based coarse-to-fine framework to efficiently tackle these challenges. The proposed 3D-based framework outperforms their 2D counterparts by a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on three datasets, the NIH pancreas dataset, the JHMI pancreas dataset and the JHMI pathological cyst dataset.
  arXiv  Detail & Related papers  (2020-10-29T15:39:19Z)
• DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation [27.547646527286886]
  Three patterns of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs. For better detecting and segmenting brain tumors from 3D MR images, CNN-based models embedded with DDCs (DDU-Nets) are trained efficiently from pixel to pixel. The proposed method is evaluated on the BraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Nets.
  arXiv  Detail & Related papers  (2020-03-03T05:08:34Z)
• 2.75D: Boosting learning by representing 3D Medical imaging to 2D features for small data [54.223614679807994]
  3D convolutional neural networks (CNNs) have started to show superior performance to 2D CNNs in numerous deep learning tasks. Applying transfer learning on 3D CNN is challenging due to a lack of publicly available pre-trained 3D models. In this work, we proposed a novel 2D strategical representation of volumetric data, namely 2.75D. As a result, 2D CNN networks can also be used to learn volumetric information.
  arXiv  Detail & Related papers  (2020-02-11T08:24:19Z)

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.