Transesophageal Echocardiography Generation using Anatomical Models

• URL: http://arxiv.org/abs/2410.06781v1
• Date: Wed, 9 Oct 2024 11:20:28 GMT
• Title: Transesophageal Echocardiography Generation using Anatomical Models
• Authors: Emmanuel Oladokun, Musa Abdulkareem, Jurica Šprem, Vicente Grau,
• Abstract summary: We develop a pipeline to generate synthetic TEE images and corresponding semantic labels. For the pipeline's unpaired image-to-image (I2I) translation section, we explore two generative methods. We achieve a dice score improvement of up to 10% when we augment datasets with our synthetic images.
• Score: 0.5679566039341877
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Through automation, deep learning (DL) can enhance the analysis of transesophageal echocardiography (TEE) images. However, DL methods require large amounts of high-quality data to produce accurate results, which is difficult to satisfy. Data augmentation is commonly used to tackle this issue. In this work, we develop a pipeline to generate synthetic TEE images and corresponding semantic labels. The proposed data generation pipeline expands on an existing pipeline that generates synthetic transthoracic echocardiography images by transforming slices from anatomical models into synthetic images. We also demonstrate that such images can improve DL network performance through a left-ventricle semantic segmentation task. For the pipeline's unpaired image-to-image (I2I) translation section, we explore two generative methods: CycleGAN and contrastive unpaired translation. Next, we evaluate the synthetic images quantitatively using the Fr\'echet Inception Distance (FID) Score and qualitatively through a human perception quiz involving expert cardiologists and the average researcher. In this study, we achieve a dice score improvement of up to 10% when we augment datasets with our synthetic images. Furthermore, we compare established methods of assessing unpaired I2I translation and observe a disagreement when evaluating the synthetic images. Finally, we see which metric better predicts the generated data's efficacy when used for data augmentation.

Related papers

• Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks [4.1942958779358674]
  This paper utilizes recent vision-language models to produce diverse and realistic synthetic echocardiography image data. We show that the rich contextual information present in the synthesized data potentially enhances the accuracy and interpretability of downstream tasks.
  arXiv  Detail & Related papers  (2024-03-28T23:26:45Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• A Domain Translation Framework with an Adversarial Denoising Diffusion Model to Generate Synthetic Datasets of Echocardiography Images [0.5999777817331317]
  We introduce a framework to create echocardiography images suitable to be used for clinical research purposes. For several domain translation operations, the results verified that such generative model was able to synthesize high quality image samples.
  arXiv  Detail & Related papers  (2024-03-07T15:58:03Z)
• LOTUS: Learning to Optimize Task-based US representations [39.81131738128329]
  Anatomical segmentation of organs in ultrasound images is essential to many clinical applications. Existing deep neural networks require a large amount of labeled data for training in order to achieve clinically acceptable performance. In this paper, we propose a novel approach for learning to optimize task-based ultra-sound image representations.
  arXiv  Detail & Related papers  (2023-07-29T16:29:39Z)
• Mask-conditioned latent diffusion for generating gastrointestinal polyp images [2.027538200191349]
  This study proposes a conditional DPM framework to generate synthetic GI polyp images conditioned on given segmentation masks. Our system can generate an unlimited number of high-fidelity synthetic polyp images with the corresponding ground truth masks of polyps. Results show that the best micro-imagewise IOU of 0.7751 was achieved from DeepLabv3+ when the training data consists of both real data and synthetic data.
  arXiv  Detail & Related papers  (2023-04-11T14:11:17Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• Sharp-GAN: Sharpness Loss Regularized GAN for Histopathology Image Synthesis [65.47507533905188]
  Conditional generative adversarial networks have been applied to generate synthetic histopathology images. We propose a sharpness loss regularized generative adversarial network to synthesize realistic histopathology images.
  arXiv  Detail & Related papers  (2021-10-27T18:54:25Z)
• CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation [56.622832383316215]
  We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans. Our approach is based on cycle-consistent generative adversarial convolutional transformers, for short, CyTran. Our empirical results show that CyTran outperforms all competing methods.
  arXiv  Detail & Related papers  (2021-10-12T23:25:03Z)
• Image Translation for Medical Image Generation -- Ischemic Stroke Lesions [0.0]
  Synthetic databases with annotated pathologies could provide the required amounts of training data. We train different image-to-image translation models to synthesize magnetic resonance images of brain volumes with and without stroke lesions. We show that for a small database of only 10 or 50 clinical cases, synthetic data augmentation yields significant improvement.
  arXiv  Detail & Related papers  (2020-10-05T09:12:28Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)

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.