Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation

Farshad, Azade; Yeganeh, Yousef; Gehlbach, Peter; Navab, Nassir

doi:10.1007/978-3-031-16434-7_56

Cited by 28 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Azade et al [ 61 ] introduced Y-Net, an architecture combining spectral and spatial domain features, to improve retinal optical coherence tomography (OCT) image segmentation. This approach outperformed the U-Net model in terms of fluid segmentation performance.…”

Section: Analysis Of Optical Coherence Tomography Imagesmentioning

confidence: 99%

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Karn

Abdulla

2023

Bioengineering

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.

show abstract

Section: Analysis Of Optical Coherence Tomography Imagesmentioning

confidence: 99%

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Karn

Abdulla

2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…Image Generation Recent advancements in image generation have predominantly stemmed from Generative Adversarial Networks [24] and diffusion models [25,26]. The research community has delved into conditional variants [27], which facilitate image generation based on various input modalities.…”

Section: Related Workmentioning

confidence: 99%

“…SESAME [47] enables users to draw a mask with semantic labels on an image to indicate the category of changed pixels. Similarly, EditGAN [48] allows users to alter object appearance by modifying a detailed object part segmentation map [49,50]. SIMSG [23] employs scene graphs as the interface, where users can manipulate images by altering the nodes or edges of a graph.…”

Section: Related Workmentioning

confidence: 99%

Representation Learning for Semantic Scene Understanding

Farshad

2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Recent advances in semantic scene understanding have underscored its growing significance in the field of computer vision. Enhanced representations can be achieved by incorporating semantic information derived from textual data and applying it to generative models for scene modeling. Nevertheless, the features extracted from text prompts may not seamlessly model a scene. Scene graphs offer a robust solution to address this challenge, serving as a powerful representation for semantic image generation and manipulation. In this study, we delve into the utilization of scene graphs for this purpose and propose novel methodologies to augment both the representation and learning processes involved in image generation and manipulation. For image generation, we examine meta-learning for producing images in unprecedented scenes and refine the generated images using an autoregressive scene graph generation model. In terms of image manipulation, we put forth a novel self-supervised method that eliminates the need for paired before-and-after data. Additionally, we boost image manipulation performance by disentangling latent and graph representations in a self-supervised manner. By evaluating the efficacy of our proposed approaches on a diverse range of publicly available benchmarks, we demonstrate their superiority, ultimately achieving state-of-the-art performance in the domain of semantic image generation and manipulation.

show abstract

“…This modification led to significant improvements in segmentation results. Similar convolution‐based Unet structured networks include UNet 3+, 10 3D UNet, 11 V‐Net, 12 and Y‐Net 13 . In recent time, due to the fire of Transformer 14 structure, many scholars also try to use it to solve the problem of medical image segmentation, such as TransUNet 15 proposed by Chen et al He embedded the ViT 16 architecture into the encoder of the UNet structure, which combines the design concepts of Transformer and U‐Net that enabling it to effectively capture both global and local features of an image, TransUNet has achieved impressive results in areas such as medical image segmentation and excels in handling large‐scale and complex medical image data.…”

Section: Introductionmentioning

confidence: 99%

Efficient spine segmentation network based on multi‐scale feature extraction and multi‐dimensional spatial attention

Xu,

Wang,

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

In spine imaging, efficient automatic segmentation is crucial for clinical decision‐making, yet current models increase accuracy at the expense of elevated parameter counts and computational complexity, complicating integration with contemporary medical devices. Addressing identified challenges, this research introduces LE‐NeXt, a spine segmentation framework utilizing multi‐dimensional spatial attention and multi‐scale feature extraction, optimizing the architecture via convolution and MLP. It integrates lightweight convolutions and attention mechanisms within an encoder‐decoder model, enhancing stage‐specific feature extraction while ensuring efficiency. Experimental analyses on VerSe and SpineWeb datasets demonstrate that LE‐NeXt outperforms the lightweight U‐NeXt, enhancing IoU accuracy from 87.7 to 89.8 on VerSe, and exceeds the performance of established networks such as U‐Net and its variants. Significantly, on SpineWeb, LE‐NeXt not only surpasses Trans U‐Net in accuracy but also achieves a considerable reduction in both parameter count and computational complexity. These results emphasize LE‐NeXt's effectiveness in improving segmentation precision efficiently, optimally balancing computational efficiency and accuracy.

show abstract

Y-Net: A Spatiospectral Dual-Encoder Network for Medical Image Segmentation

Cited by 28 publications

References 30 publications

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Representation Learning for Semantic Scene Understanding

Efficient spine segmentation network based on multi‐scale feature extraction and multi‐dimensional spatial attention

Contact Info

Product

Resources

About