Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement

• URL: http://arxiv.org/abs/2202.12474v1
• Date: Fri, 25 Feb 2022 03:09:46 GMT
• Title: Structure-aware Unsupervised Tagged-to-Cine MRI Synthesis with Self Disentanglement
• Abstract summary: We learn a general add-on structural feature extractor, by explicitly enforcing the structural alignment between an input and its synthesized image. Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities. We train, validate, and test our network on 1,768, 416, and 1,560 unpaired subject-independent slices of tagged and cine magnetic resonance imaging.
• Score: 13.442093381065268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cycle reconstruction regularized adversarial training -- e.g., CycleGAN, DiscoGAN, and DualGAN -- has been widely used for image style transfer with unpaired training data. Several recent works, however, have shown that local distortions are frequent, and structural consistency cannot be guaranteed. Targeting this issue, prior works usually relied on additional segmentation or consistent feature extraction steps that are task-specific. To counter this, this work aims to learn a general add-on structural feature extractor, by explicitly enforcing the structural alignment between an input and its synthesized image. Specifically, we propose a novel input-output image patches self-training scheme to achieve a disentanglement of underlying anatomical structures and imaging modalities. The translator and structure encoder are updated, following an alternating training protocol. In addition, the information w.r.t. imaging modality can be eliminated with an asymmetric adversarial game. We train, validate, and test our network on 1,768, 416, and 1,560 unpaired subject-independent slices of tagged and cine magnetic resonance imaging from a total of twenty healthy subjects, respectively, demonstrating superior performance over competing methods.

Related papers

• SegDem: Segmentation helps Demosaicing [52.515779223891705]
  Image demosaicing reconstructs a full-color image from incomplete color measurements produced by a sensor covered with a color filter (CFA)<n>Most existing methods formulate demosaicing as pixel-level reconstruction.<n>SegDem is a cross-task decoder representation transfer framework for demosaicing.
  arXiv  Detail & Related papers  (2026-08-08T04:49:20Z)
• SpectraFlow: Unifying Structural Pretraining and Frequency Adaptation for Medical Image Segmentation [3.182068203318624]
  Recent self-supervised pretraining has improved transferability, but it often exhibits a texture bias.<n>We propose a two-stage framework that couples structure-aware encoder pretraining with boundary-oriented decoding.<n> Experiments on ISIC-2016, Kvasir-SEG, and GlaS demonstrate consistent gains over state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-14T08:37:10Z)
• Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation [51.509572354327986]
  This work introduces a novel two-stage (structure- and report-learning) framework tailored for Computed Tomography Report Generation (CTRG)<n>In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss.<n>In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption.
  arXiv  Detail & Related papers  (2026-03-05T07:07:07Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising [1.912429179274357]
  Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions.<n>We propose a zero-shot denoising framework tailored for low-angle CPWC acquisitions.
  arXiv  Detail & Related papers  (2025-06-26T17:28:32Z)
• "Principal Components" Enable A New Language of Images [79.45806370905775]
  We introduce a novel visual tokenization framework that embeds a provable PCA-like structure into the latent token space. Our approach achieves state-of-the-art reconstruction performance and enables better interpretability to align with the human vision system.
  arXiv  Detail & Related papers  (2025-03-11T17:59:41Z)
• Structural Attention: Rethinking Transformer for Unpaired Medical Image Synthesis [25.531060259709157]
  Transformer-based models excel in imaging translation tasks thanks to their ability to capture long-range dependencies. This paper empirically demonstrates that, lacking strong inductive biases, Transformer can converge to non-optimal solutions in the absence of paired data. We introduce UNet Structured Transformer (UNest), a novel architecture incorporating structural inductive biases for unpaired medical image synthesis.
  arXiv  Detail & Related papers  (2024-06-27T07:59:25Z)
• Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction [13.277067849874756]
  We study how DIP recovers information from undersampled imaging measurements. We introduce a self-driven reconstruction process that concurrently optimize both the network weights and the input. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image.
  arXiv  Detail & Related papers  (2024-02-06T15:52:23Z)
• 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)
• Structure-aware registration network for liver DCE-CT images [50.28546654316009]
  We propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Our proposed method can achieve higher registration accuracy and preserve anatomical structure more effectively than state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-08T14:08:56Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image Segmentation [8.772764547425291]
  CISFA builds on image domain translation and unsupervised feature adaptation for cross-modality biomedical image segmentation. We use a one-sided generative model and add a weighted patch-wise contrastive loss between sampled patches of the input image and the corresponding synthetic image. We evaluate our methods on segmentation tasks containing CT and MRI images for abdominal cavities and whole hearts.
  arXiv  Detail & Related papers  (2022-07-27T01:49:26Z)
• 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)
• Towards a Unified Foundation Model: Jointly Pre-Training Transformers on Unpaired Images and Text [93.11954811297652]
  We design a unified transformer consisting of modality-specific tokenizers, a shared transformer encoder, and task-specific output heads. We employ the separately-trained BERT and ViT models as teachers and apply knowledge distillation to provide additional, accurate supervision signals. Experiments show that the resultant unified foundation transformer works surprisingly well on both the vision-only and text-only tasks.
  arXiv  Detail & Related papers  (2021-12-14T00:20:55Z)
• Structure Destruction and Content Combination for Face Anti-Spoofing [14.413900076141093]
  Anti-spoofing studies excavate the hidden cues in original images to discriminate real persons and diverse attack types. These methods are prone to stick on memorization of the entire training dataset and show sensitivity to nonhomologous domain distribution. We propose Structure Destruction Module and Content Combination Module to address these two imitations.
  arXiv  Detail & Related papers  (2021-07-22T13:08:46Z)
• Towards Unsupervised Learning for Instrument Segmentation in Robotic Surgery with Cycle-Consistent Adversarial Networks [54.00217496410142]
  We propose an unpaired image-to-image translation where the goal is to learn the mapping between an input endoscopic image and a corresponding annotation. Our approach allows to train image segmentation models without the need to acquire expensive annotations. We test our proposed method on Endovis 2017 challenge dataset and show that it is competitive with supervised segmentation methods.
  arXiv  Detail & Related papers  (2020-07-09T01:39:39Z)
• Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images [0.0]
  This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation. It relies on the benefit of unsupervised learning, in the form of image reconstruction, for network training. Our experiments demonstrate that it leads to better segmentation results in these datasets, compared to its counterparts.
  arXiv  Detail & Related papers  (2020-01-30T08:09:38Z)

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.