Framework for lung CT image segmentation based on UNet++
- URL: http://arxiv.org/abs/2501.02428v1
- Date: Sun, 05 Jan 2025 03:23:39 GMT
- Title: Framework for lung CT image segmentation based on UNet++
- Authors: Hao Ziang, Jingsi Zhang, Lixian Li,
- Abstract summary: We propose a new whole-process network merging advanced UNet++ model.
By incorporating diverse methods, the training results demonstrate a significant advantage over similar works.
Our network is remarkable as one of the first to target on lung slice CT images.
- Score: 0.4915744683251151
- License:
- Abstract: Recently, the state-of-art models for medical image segmentation is U-Net and their variants. These networks, though succeeding in deriving notable results, ignore the practical problem hanging over the medical segmentation field: overfitting and small dataset. The over-complicated deep neural networks unnecessarily extract meaningless information, and a majority of them are not suitable for lung slice CT image segmentation task. To overcome the two limitations, we proposed a new whole-process network merging advanced UNet++ model. The network comprises three main modules: data augmentation, optimized neural network, parameter fine-tuning. By incorporating diverse methods, the training results demonstrate a significant advantage over similar works, achieving leading accuracy of 98.03% with the lowest overfitting. potential. Our network is remarkable as one of the first to target on lung slice CT images.
Related papers
- A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation [0.0]
Convolutional neural networks (CNNs) have dominated the field, achieving significant success in 3D medical image segmentation.
Recent transformer-based models, such as TransUNet and nnFormer, have demonstrated promise in addressing these limitations.
This paper introduces a novel, fully convolutional-free model based on transformer architecture and self-attention mechanisms.
arXiv Detail & Related papers (2025-02-08T00:52:45Z) - WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep
Imaging Ultrasound [1.2903292694072621]
Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture quality ultrasound images.
We present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet)
In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned.
arXiv Detail & Related papers (2023-11-17T20:32:37Z) - Connecting the Dots: Graph Neural Network Powered Ensemble and
Classification of Medical Images [0.0]
Deep learning for medical imaging is limited due to the requirement for large amounts of training data.
We employ the Image Foresting Transform to optimally segment images into superpixels.
These superpixels are subsequently transformed into graph-structured data, enabling the proficient extraction of features and modeling of relationships.
arXiv Detail & Related papers (2023-11-13T13:20:54Z) - LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical
Imaging via Second-order Graph Matching [59.01894976615714]
We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets.
We have collected approximately 1.3 million medical images from 55 publicly available datasets.
LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
arXiv Detail & Related papers (2023-06-20T22:21:34Z) - Improved distinct bone segmentation in upper-body CT through
multi-resolution networks [0.39583175274885335]
In distinct bone segmentation from upper body CTs a large field of view and a computationally taxing 3D architecture are required.
This leads to low-resolution results lacking detail or localisation errors due to missing spatial context.
We propose end-to-end trainable segmentation networks that combine several 3D U-Nets working at different resolutions.
arXiv Detail & Related papers (2023-01-31T14:46:16Z) - Dual Multi-scale Mean Teacher Network for Semi-supervised Infection
Segmentation in Chest CT Volume for COVID-19 [76.51091445670596]
Automated detecting lung infections from computed tomography (CT) data plays an important role for combating COVID-19.
Most current COVID-19 infection segmentation methods mainly relied on 2D CT images, which lack 3D sequential constraint.
Existing 3D CT segmentation methods focus on single-scale representations, which do not achieve the multiple level receptive field sizes on 3D volume.
arXiv Detail & Related papers (2022-11-10T13:11:21Z) - Two-Stream Graph Convolutional Network for Intra-oral Scanner Image
Segmentation [133.02190910009384]
We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes.
Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
arXiv Detail & Related papers (2022-04-19T10:41:09Z) - Multi-organ Segmentation Network with Adversarial Performance Validator [10.775440368500416]
This paper introduces an adversarial performance validation network into a 2D-to-3D segmentation framework.
The proposed network converts the 2D-coarse result to 3D high-quality segmentation masks in a coarse-to-fine manner, allowing joint optimization to improve segmentation accuracy.
Experiments on the NIH pancreas segmentation dataset demonstrate the proposed network achieves state-of-the-art accuracy on small organ segmentation and outperforms the previous best.
arXiv Detail & Related papers (2022-04-16T18:00:29Z) - InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal
Artifact Reduction in CT Images [53.4351366246531]
We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.
We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
arXiv Detail & Related papers (2021-12-23T15:52:37Z) - 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) - Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19.
segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues.
To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
arXiv Detail & Related papers (2020-04-22T07:30:56Z)
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.