SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution

• URL: http://arxiv.org/abs/2409.01013v1
• Date: Mon, 2 Sep 2024 07:45:06 GMT
• Title: SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
• Authors: Mevan Ekanayake, Zhifeng Chen, Gary Egan, Mehrtash Harandi, Zhaolin Chen,
• Abstract summary: Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals. We propose a novel framework, referred to as the Semantically Conditioned INR (SeCo-INR), that conditions an INR using local priors from a medical image. Our framework learns a continuous representation of the semantic segmentation features of a medical image and utilizes it to derive the optimal INR for each semantic region of the image.
• Score: 25.078280843551322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit Neural Representations (INRs) have recently advanced the field of deep learning due to their ability to learn continuous representations of signals without the need for large training datasets. Although INR methods have been studied for medical image super-resolution, their adaptability to localized priors in medical images has not been extensively explored. Medical images contain rich anatomical divisions that could provide valuable local prior information to enhance the accuracy and robustness of INRs. In this work, we propose a novel framework, referred to as the Semantically Conditioned INR (SeCo-INR), that conditions an INR using local priors from a medical image, enabling accurate model fitting and interpolation capabilities to achieve super-resolution. Our framework learns a continuous representation of the semantic segmentation features of a medical image and utilizes it to derive the optimal INR for each semantic region of the image. We tested our framework using several medical imaging modalities and achieved higher quantitative scores and more realistic super-resolution outputs compared to state-of-the-art methods.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Disruptive Autoencoders: Leveraging Low-level features for 3D Medical Image Pre-training [51.16994853817024]
  This work focuses on designing an effective pre-training framework for 3D radiology images. We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations. The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-07-31T17:59:42Z)
• Implicit Neural Representation in Medical Imaging: A Comparative Survey [3.478921293603811]
  Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics. This survey aims to provide a comprehensive overview of INR models in the field of medical imaging.
  arXiv  Detail & Related papers  (2023-07-30T06:39:25Z)
• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Data-Consistent Local Superresolution for Medical Imaging [5.025654873456756]
  After a reconstruction of a full tomographic image, the clinician may believe that some critical parts of the image are not clear enough, and may wish to see clearer these regions-of-interest. A naive approach (which is highly not recommended) would be performing the global reconstruction of a higher resolution image. We propose a new paradigm of iterative model-based reconstruction algorithms for providing real-time solution for zooming-in and refining a region of interest.
  arXiv  Detail & Related papers  (2022-02-22T13:18:38Z)
• Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation [0.22843885788439805]
  Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distribution. This thesis contains four GAN projects aiming to present such novel applications' clinical relevance in collaboration with physicians.
  arXiv  Detail & Related papers  (2021-06-03T15:08:14Z)
• MIASSR: An Approach for Medical Image Arbitrary Scale Super-Resolution [3.0554209431226624]
  Single image super-resolution aims to obtain a high-resolution output from one low-resolution image. Deep learning-based SISR approaches have been widely discussed in medical image processing. We propose an approach for medical image arbitrary-scale super-resolution (MIASSR)
  arXiv  Detail & Related papers  (2021-05-22T14:24:25Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation [9.659642285903418]
  Cross-modality synthesis of medical images to reduce the costly annotation burden by radiologists. We present a novel approach for image-to-image translation in medical images, capable of supervised or unsupervised (unpaired image data) setups.
  arXiv  Detail & Related papers  (2021-03-05T16:22:31Z)
• Few-shot Medical Image Segmentation using a Global Correlation Network with Discriminative Embedding [60.89561661441736]
  We propose a novel method for few-shot medical image segmentation. We construct our few-shot image segmentor using a deep convolutional network trained episodically. We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
  arXiv  Detail & Related papers  (2020-12-10T04:01:07Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)

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.