Med-TTT: Vision Test-Time Training model for Medical Image Segmentation
- URL: http://arxiv.org/abs/2410.02523v1
- Date: Thu, 3 Oct 2024 14:29:46 GMT
- Title: Med-TTT: Vision Test-Time Training model for Medical Image Segmentation
- Authors: Jiashu Xu,
- Abstract summary: We propose Med-TTT, a visual backbone network integrated with Test-Time Training layers.
The model achieves leading performance in terms of accuracy, sensitivity, and Dice coefficient.
- Score: 5.318153305245246
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Medical image segmentation plays a crucial role in clinical diagnosis and treatment planning. Although models based on convolutional neural networks (CNNs) and Transformers have achieved remarkable success in medical image segmentation tasks, they still face challenges such as high computational complexity and the loss of local features when capturing long-range dependencies. To address these limitations, we propose Med-TTT, a visual backbone network integrated with Test-Time Training (TTT) layers, which incorporates dynamic adjustment capabilities. Med-TTT introduces the Vision-TTT layer, which enables effective modeling of long-range dependencies with linear computational complexity and adaptive parameter adjustment during inference. Furthermore, we designed a multi-resolution fusion mechanism to combine image features at different scales, facilitating the identification of subtle lesion characteristics in complex backgrounds. At the same time, we adopt a frequency domain feature enhancement strategy based on high pass filtering, which can better capture texture and fine-grained details in images. Experimental results demonstrate that Med-TTT significantly outperforms existing methods on multiple medical image datasets, exhibiting strong segmentation capabilities, particularly in complex image backgrounds. The model achieves leading performance in terms of accuracy, sensitivity, and Dice coefficient, providing an efficient and robust solution for the field of medical image segmentation.The code is available at https://github.com/Jiashu-Xu/Med-TTT .
Related papers
- TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation [28.21682021877434]
TTT-Unet is a novel framework that integrates Test-Time Training layers into the traditional U-Net architecture for biomedical image segmentation.
We evaluate TTT-Unet on multiple medical imaging datasets, including 3D abdominal organ segmentation in CT and MR images, instrument segmentation in endoscopy images, and cell segmentation in microscopy images.
arXiv Detail & Related papers (2024-09-17T15:52:40Z) - PMT: Progressive Mean Teacher via Exploring Temporal Consistency for Semi-Supervised Medical Image Segmentation [51.509573838103854]
We propose a semi-supervised learning framework, termed Progressive Mean Teachers (PMT), for medical image segmentation.
Our PMT generates high-fidelity pseudo labels by learning robust and diverse features in the training process.
Experimental results on two datasets with different modalities, i.e., CT and MRI, demonstrate that our method outperforms the state-of-the-art medical image segmentation approaches.
arXiv Detail & Related papers (2024-09-08T15:02:25Z) - TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation [6.013821375459473]
We introduce a novel deep learning architecture for medical image segmentation.
Our proposed model shows consistent improvement over the state of the art on ten publicly available datasets.
arXiv Detail & Related papers (2024-09-05T09:14:03Z) - QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge.
This variability directly impacts the development and evaluation of automated segmentation algorithms.
We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
arXiv Detail & Related papers (2024-03-19T17:57:24Z) - Enhancing CT Image synthesis from multi-modal MRI data based on a
multi-task neural network framework [16.864720020158906]
We propose a versatile multi-task neural network framework, based on an enhanced Transformer U-Net architecture.
We decompose the traditional problem of synthesizing CT images into distinct subtasks.
To enhance the framework's versatility in handling multi-modal data, we expand the model with multiple image channels.
arXiv Detail & Related papers (2023-12-13T18:22:38Z) - 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) - Multimodal-Boost: Multimodal Medical Image Super-Resolution using
Multi-Attention Network with Wavelet Transform [5.416279158834623]
Loss of corresponding image resolution degrades the overall performance of medical image diagnosis.
Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework.
This work proposes generative adversarial network (GAN) with deep multi-attention modules to learn high-frequency information from low-frequency data.
arXiv Detail & Related papers (2021-10-22T10:13:46Z) - TransUNet: Transformers Make Strong Encoders for Medical Image
Segmentation [78.01570371790669]
Medical image segmentation is an essential prerequisite for developing healthcare systems.
On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the de-facto standard.
We propose TransUNet, which merits both Transformers and U-Net, as a strong alternative for medical image segmentation.
arXiv Detail & Related papers (2021-02-08T16:10:50Z) - Max-Fusion U-Net for Multi-Modal Pathology Segmentation with Attention
and Dynamic Resampling [13.542898009730804]
The performance of relevant algorithms is significantly affected by the proper fusion of the multi-modal information.
We present the Max-Fusion U-Net that achieves improved pathology segmentation performance.
We evaluate our methods using the Myocardial pathology segmentation (MyoPS) combining the multi-sequence CMR dataset.
arXiv Detail & Related papers (2020-09-05T17:24:23Z) - Learning Deformable Image Registration from Optimization: Perspective,
Modules, Bilevel Training and Beyond [62.730497582218284]
We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation.
We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
arXiv Detail & Related papers (2020-04-30T03:23:45Z) - Pathological Retinal Region Segmentation From OCT Images Using Geometric
Relation Based Augmentation [84.7571086566595]
We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape.
The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
arXiv Detail & Related papers (2020-03-31T11:50:43Z)
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.