E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
- URL: http://arxiv.org/abs/2312.04727v2
- Date: Wed, 19 Feb 2025 08:52:07 GMT
- Title: E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation
- Authors: Boqian Wu, Qiao Xiao, Shiwei Liu, Lu Yin, Mykola Pechenizkiy, Decebal Constantin Mocanu, Maurice Van Keulen, Elena Mocanu,
- Abstract summary: 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.
- Score: 34.865695471451886
- License:
- Abstract: Deep neural networks have evolved as the leading approach in 3D medical image segmentation due to their outstanding performance. However, the ever-increasing model size and computation cost of deep neural networks have become the primary barrier to deploying them on real-world resource-limited hardware. In pursuit of improving performance and efficiency, we propose a 3D medical image segmentation model, named Efficient to Efficient Network (E2ENet), incorporating two parametrically and computationally efficient designs. i. Dynamic sparse feature fusion (DSFF) mechanism: it adaptively learns to fuse informative multi-scale features while reducing redundancy. ii. Restricted depth-shift in 3D convolution: it leverages the 3D spatial information while keeping the model and computational complexity as 2D-based methods. We conduct extensive experiments on BTCV, AMOS-CT and Brain Tumor Segmentation Challenge, demonstrating that E2ENet consistently achieves a superior trade-off between accuracy and efficiency than prior arts across various resource constraints. E2ENet achieves comparable accuracy on the large-scale challenge AMOS-CT, while saving over 68\% parameter count and 29\% FLOPs in the inference phase, compared with the previous best-performing method. Our code has been made available at: https://github.com/boqian333/E2ENet-Medical.
Related papers
- Intensity-Spatial Dual Masked Autoencoder for Multi-Scale Feature Learning in Chest CT Segmentation [4.916334618361524]
This paper proposes an improved method named Intensity-Spatial Dual Masked AutoEncoder (ISD-MAE)
The model utilizes a dual-branch structure and contrastive learning to enhance the ability to learn tissue features and boundary details.
The results show that ISD-MAE significantly outperforms other methods in 2D pneumonia and mediastinal tumor segmentation tasks.
arXiv Detail & Related papers (2024-11-20T10:58:47Z) - EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation [3.6813810514531085]
We introduce a novel 3D medical image segmentation model called EM-Net. Inspired by its success, we introduce a novel Mamba-based 3D medical image segmentation model called EM-Net.
Comprehensive experiments on two challenging multi-organ datasets with other state-of-the-art (SOTA) algorithms show that our method exhibits better segmentation accuracy while requiring nearly half the parameter size of SOTA models and 2x faster training speed.
arXiv Detail & Related papers (2024-09-26T09:34:33Z) - LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors.
We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption.
LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
arXiv Detail & Related papers (2024-07-13T16:09:45Z) - Spatiotemporal Modeling Encounters 3D Medical Image Analysis:
Slice-Shift UNet with Multi-View Fusion [0.0]
We propose a new 2D-based model dubbed Slice SHift UNet which encodes three-dimensional features at 2D CNN's complexity.
More precisely multi-view features are collaboratively learned by performing 2D convolutions along the three planes of a volume.
The effectiveness of our approach is validated in Multi-Modality Abdominal Multi-Organ axis (AMOS) and Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) datasets.
arXiv Detail & Related papers (2023-07-24T14:53:23Z) - 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation.
Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
arXiv Detail & Related papers (2023-06-23T12:09:52Z) - UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation [93.88170217725805]
We propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed.
The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features.
Our evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy.
arXiv Detail & Related papers (2022-12-08T18:59:57Z) - GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction [50.248694764703714]
Unrolled neural networks have recently achieved state-of-the-art accelerated MRI reconstruction.
These networks unroll iterative optimization algorithms by alternating between physics-based consistency and neural-network based regularization.
We propose Greedy LEarning for Accelerated MRI reconstruction, an efficient training strategy for high-dimensional imaging settings.
arXiv Detail & Related papers (2022-07-18T06:01:29Z) - MISSU: 3D Medical Image Segmentation via Self-distilling TransUNet [55.16833099336073]
We propose to self-distill a Transformer-based UNet for medical image segmentation.
It simultaneously learns global semantic information and local spatial-detailed features.
Our MISSU achieves the best performance over previous state-of-the-art methods.
arXiv Detail & Related papers (2022-06-02T07:38:53Z) - CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image
Segmentation [95.51455777713092]
Convolutional neural networks (CNNs) have been the de facto standard for nowadays 3D medical image segmentation.
We propose a novel framework that efficiently bridges a bf Convolutional neural network and a bf Transformer bf (CoTr) for accurate 3D medical image segmentation.
arXiv Detail & Related papers (2021-03-04T13:34:22Z) - Efficient embedding network for 3D brain tumor segmentation [0.33727511459109777]
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.
arXiv Detail & Related papers (2020-11-22T16:17:29Z)
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.