MAISI: Medical AI for Synthetic Imaging

• URL: http://arxiv.org/abs/2409.11169v2
• Date: Tue, 29 Oct 2024 19:17:36 GMT
• Title: MAISI: Medical AI for Synthetic Imaging
• Authors: Pengfei Guo, Can Zhao, Dong Yang, Ziyue Xu, Vishwesh Nath, Yucheng Tang, Benjamin Simon, Mason Belue, Stephanie Harmon, Baris Turkbey, Daguang Xu,
• Abstract summary: Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI) to generate synthetic 3D computed tomography (CT) images.
• Score: 16.687814167558326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical imaging analysis faces challenges such as data scarcity, high annotation costs, and privacy concerns. This paper introduces the Medical AI for Synthetic Imaging (MAISI), an innovative approach using the diffusion model to generate synthetic 3D computed tomography (CT) images to address those challenges. MAISI leverages the foundation volume compression network and the latent diffusion model to produce high-resolution CT images (up to a landmark volume dimension of 512 x 512 x 768 ) with flexible volume dimensions and voxel spacing. By incorporating ControlNet, MAISI can process organ segmentation, including 127 anatomical structures, as additional conditions and enables the generation of accurately annotated synthetic images that can be used for various downstream tasks. Our experiment results show that MAISI's capabilities in generating realistic, anatomically accurate images for diverse regions and conditions reveal its promising potential to mitigate challenges using synthetic data.

Related papers

• Unifying Subsampling Pattern Variations for Compressed Sensing MRI with Neural Operators [72.79532467687427]
  Compressed Sensing MRI reconstructs images of the body's internal anatomy from undersampled and compressed measurements. Deep neural networks have shown great potential for reconstructing high-quality images from highly undersampled measurements. We propose a unified model that is robust to different subsampling patterns and image resolutions in CS-MRI.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models [48.87160158792048]
  We introduce a cascaded amortized latent diffusion model (CA-LDM) that can synthesis high-resolution OCT volumes in a memory-efficient way. Experiments on a public high-resolution OCT dataset show that our synthetic data have realistic high-resolution and global features, surpassing the capabilities of existing methods.
  arXiv  Detail & Related papers  (2024-05-26T10:58:22Z)
• WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis [1.647759094903376]
  This work presents WDM, a wavelet-based medical image synthesis framework that applies a diffusion model on wavelet images. Experimental results on BraTS and LIDC-IDRI unconditional image generation at a resolution of $128 times 128 times 128$ demonstrate state-of-the-art image fidelity (FID) and sample diversity (MS-SSIM) scores. Our proposed method is the only one capable of generating high-quality images at a resolution of $256 times 256 times 256$, outperforming all comparing methods.
  arXiv  Detail & Related papers  (2024-02-29T11:11:05Z)
• Retinal OCT Synthesis with Denoising Diffusion Probabilistic Models for Layer Segmentation [2.4113205575263708]
  We propose an image synthesis method that utilizes denoising diffusion probabilistic models (DDPMs) to automatically generate retinal optical coherence tomography ( OCT) images. We observe a consistent improvement in layer segmentation accuracy, which is validated using various neural networks. These findings demonstrate the promising potential of DDPMs in reducing the need for manual annotations of retinal OCT images.
  arXiv  Detail & Related papers  (2023-11-09T16:09:24Z)
• A 3D generative model of pathological multi-modal MR images and segmentations [3.4806591877889375]
  We propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data.
  arXiv  Detail & Related papers  (2023-11-08T09:36:37Z)
• EMIT-Diff: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model [4.057796755073023]
  We develop controllable diffusion models for medical image synthesis, called EMIT-Diff. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data. In our approach, we ensure that the synthesized samples adhere to medically relevant constraints.
  arXiv  Detail & Related papers  (2023-10-19T16:18:02Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• 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)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN [52.851990439671475]
  We propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation.
  arXiv  Detail & Related papers  (2020-08-05T02:33:04Z)

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.