BreakNet: Discontinuity-Resilient Multi-Scale Transformer Segmentation of Retinal Layers
- URL: http://arxiv.org/abs/2408.14606v1
- Date: Mon, 26 Aug 2024 19:59:20 GMT
- Title: BreakNet: Discontinuity-Resilient Multi-Scale Transformer Segmentation of Retinal Layers
- Authors: Razieh Ganjee, Bingjie Wang, Lingyun Wang, Chengcheng Zhao, José-Alain Sahel, Shaohua Pi,
- Abstract summary: BreakNet is a Transformer-based segmentation model designed to address boundary discontinuities caused by shadow artifacts.
Our findings indicate that BreakNet has the potential to significantly improve retinal quantification and analysis.
- Score: 0.8953337264557399
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Visible light optical coherence tomography (vis-OCT) is gaining traction for retinal imaging due to its high resolution and functional capabilities. However, the significant absorption of hemoglobin in the visible light range leads to pronounced shadow artifacts from retinal blood vessels, posing challenges for accurate layer segmentation. In this study, we present BreakNet, a multi-scale Transformer-based segmentation model designed to address boundary discontinuities caused by these shadow artifacts. BreakNet utilizes hierarchical Transformer and convolutional blocks to extract multi-scale global and local feature maps, capturing essential contextual, textural, and edge characteristics. The model incorporates decoder blocks that expand pathwaproys to enhance the extraction of fine details and semantic information, ensuring precise segmentation. Evaluated on rodent retinal images acquired with prototype vis-OCT, BreakNet demonstrated superior performance over state-of-the-art segmentation models, such as TCCT-BP and U-Net, even when faced with limited-quality ground truth data. Our findings indicate that BreakNet has the potential to significantly improve retinal quantification and analysis.
Related papers
- TransResNet: Integrating the Strengths of ViTs and CNNs for High Resolution Medical Image Segmentation via Feature Grafting [6.987177704136503]
High-resolution images are preferable in medical imaging domain as they significantly improve the diagnostic capability of the underlying method.
Most of the existing deep learning-based techniques for medical image segmentation are optimized for input images having small spatial dimensions and perform poorly on high-resolution images.
We propose a parallel-in-branch architecture called TransResNet, which incorporates Transformer and CNN in a parallel manner to extract features from multi-resolution images independently.
arXiv Detail & Related papers (2024-10-01T18:22:34Z) - Light-weight Retinal Layer Segmentation with Global Reasoning [14.558920359236572]
We propose LightReSeg for retinal layer segmentation which can be applied to OCT images.
Our approach achieves a better segmentation performance compared to the current state-of-the-art method TransUnet.
arXiv Detail & Related papers (2024-04-25T05:42:41Z) - Slicer Networks [8.43960865813102]
We propose the Slicer Network, a novel architecture for medical image analysis.
The Slicer Network strategically refines and upsamples feature maps via a splatting-blurring-slicing process.
Experiments across different medical imaging applications have verified the Slicer Network's improved accuracy and efficiency.
arXiv Detail & Related papers (2024-01-18T09:50:26Z) - Distance Weighted Trans Network for Image Completion [52.318730994423106]
We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components.
CNNs are used to augment the local texture information of coarse priors.
DWT blocks are used to recover certain coarse textures and coherent visual structures.
arXiv Detail & Related papers (2023-10-11T12:46:11Z) - LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based
CNN for Retinal Blood Vessel Segmentation [0.0]
Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images.
Deep learning has shown promise in medical image segmentation, but its reliance on repeated convolution and pooling operations can hinder the representation of edge information.
We propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters.
arXiv Detail & Related papers (2023-09-10T09:03:53Z) - Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network.
We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module.
Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
arXiv Detail & Related papers (2022-12-01T07:32:56Z) - Cross-receptive Focused Inference Network for Lightweight Image
Super-Resolution [64.25751738088015]
Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks.
Transformers that need to incorporate contextual information to extract features dynamically are neglected.
We propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer.
arXiv Detail & Related papers (2022-07-06T16:32:29Z) - DepthFormer: Exploiting Long-Range Correlation and Local Information for
Accurate Monocular Depth Estimation [50.08080424613603]
Long-range correlation is essential for accurate monocular depth estimation.
We propose to leverage the Transformer to model this global context with an effective attention mechanism.
Our proposed model, termed DepthFormer, surpasses state-of-the-art monocular depth estimation methods with prominent margins.
arXiv Detail & Related papers (2022-03-27T05:03:56Z) - 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) - PnP-DETR: Towards Efficient Visual Analysis with Transformers [146.55679348493587]
Recently, DETR pioneered the solution vision tasks with transformers, it directly translates the image feature map into the object result.
Recent transformer-based image recognition model andTT show consistent efficiency gain.
arXiv Detail & Related papers (2021-09-15T01:10:30Z)
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.