Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation

• URL: http://arxiv.org/abs/2312.03043v1
• Date: Tue, 5 Dec 2023 16:20:22 GMT
• Title: Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
• Authors: Simeon Allmendinger, Patrick Hemmer, Moritz Queisner, Igor Sauer, Leopold M\"uller, Johannes Jakubik, Michael V\"ossing, Niklas K\"uhl
• Abstract summary: We present an intuitive approach for generating synthetic laparoscopic images from short text prompts using diffusion-based generative models. Results on fidelity and diversity demonstrate that diffusion-based models can acquire knowledge about the style and semantics in the field of image-guided surgery.
• Score: 3.2039076408339353
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in synthetic imaging open up opportunities for obtaining additional data in the field of surgical imaging. This data can provide reliable supplements supporting surgical applications and decision-making through computer vision. Particularly the field of image-guided surgery, such as laparoscopic and robotic-assisted surgery, benefits strongly from synthetic image datasets and virtual surgical training methods. Our study presents an intuitive approach for generating synthetic laparoscopic images from short text prompts using diffusion-based generative models. We demonstrate the usage of state-of-the-art text-to-image architectures in the context of laparoscopic imaging with regard to the surgical removal of the gallbladder as an example. Results on fidelity and diversity demonstrate that diffusion-based models can acquire knowledge about the style and semantics in the field of image-guided surgery. A validation study with a human assessment survey underlines the realistic nature of our synthetic data, as medical personnel detects actual images in a pool with generated images causing a false-positive rate of 66%. In addition, the investigation of a state-of-the-art machine learning model to recognize surgical actions indicates enhanced results when trained with additional generated images of up to 5.20%. Overall, the achieved image quality contributes to the usage of computer-generated images in surgical applications and enhances its path to maturity.

Related papers

• SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models [1.6189876649941652]
  We introduce emphSurgicaL-CD, a consistency-distilled diffusion method to generate realistic surgical images. Our results demonstrate that our method outperforms GANs and diffusion-based approaches.
  arXiv  Detail & Related papers  (2024-08-19T09:19:25Z)
• Surgical Text-to-Image Generation [1.958913666074613]
  We adapt text-to-image generative models for the surgical domain using the CholecT50 dataset. We develop Surgical Imagen to generate photorealistic and activity-aligned surgical images from triplet-based textual prompts.
  arXiv  Detail & Related papers  (2024-07-12T12:49:11Z)
• Interactive Generation of Laparoscopic Videos with Diffusion Models [1.5488613349551188]
  We show how to generate realistic laparoscopic images and videos by specifying a surgical action through text. We demonstrate the performance of our approach using the publicly available Cholec dataset family. We achieve an FID of 38.097 and an F1-score of 0.71.
  arXiv  Detail & Related papers  (2024-04-23T12:36:07Z)
• Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization [62.157627519792946]
  We introduce a novel framework called bridged transfer, which initially employs synthetic images for fine-tuning a pre-trained model to improve its transferability. We propose dataset style inversion strategy to improve the stylistic alignment between synthetic and real images. Our proposed methods are evaluated across 10 different datasets and 5 distinct models, demonstrating consistent improvements.
  arXiv  Detail & Related papers  (2024-03-28T22:25:05Z)
• 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)
• Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data [9.21828361691977]
  This study tackles key obstacles in adopting surgical navigation in orthopedic surgeries. It shows an approach for generating 3D anatomical models of the spine from only a few fluoroscopic images. It achieved an 84% F1 score, matching the accuracy of our previous synthetic data-based research.
  arXiv  Detail & Related papers  (2024-01-29T10:22:45Z)
• AiAReSeg: Catheter Detection and Segmentation in Interventional Ultrasound using Transformers [75.20925220246689]
  endovascular surgeries are performed using the golden standard of Fluoroscopy, which uses ionising radiation to visualise catheters and vasculature. This work proposes a solution using an adaptation of a state-of-the-art machine learning transformer architecture to detect and segment catheters in axial interventional Ultrasound image sequences.
  arXiv  Detail & Related papers  (2023-09-25T19:34:12Z)
• SyntheX: Scaling Up Learning-based X-ray Image Analysis Through In Silico Experiments [12.019996672009375]
  We show that creating realistic simulated images from human models is a viable alternative to large-scale in situ data collection. Because synthetic generation of training data from human-based models scales easily, we find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real data-trained models.
  arXiv  Detail & Related papers  (2022-06-13T13:08:41Z)
• Semantic segmentation of multispectral photoacoustic images using deep learning [53.65837038435433]
  Photoacoustic imaging has the potential to revolutionise healthcare. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. We present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images.
  arXiv  Detail & Related papers  (2021-05-20T09:33:55Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• 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)

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.