Guided Synthesis of Labeled Brain MRI Data Using Latent Diffusion Models for Segmentation of Enlarged Ventricles

• URL: http://arxiv.org/abs/2411.01351v1
• Date: Sat, 02 Nov 2024 19:44:10 GMT
• Title: Guided Synthesis of Labeled Brain MRI Data Using Latent Diffusion Models for Segmentation of Enlarged Ventricles
• Authors: Tim Ruschke, Jonathan Frederik Carlsen, Adam Espe Hansen, Ulrich Lindberg, Amalie Monberg Hindsholm, Martin Norgaard, Claes Nøhr Ladefoged,
• Abstract summary: Deep learning models in medical contexts face challenges like data scarcity, inhomogeneity, and privacy concerns. This study focuses on improving ventricular segmentation in brain MRI images using synthetic data.
• Score: 0.4188114563181614
• License:
• Abstract: Deep learning models in medical contexts face challenges like data scarcity, inhomogeneity, and privacy concerns. This study focuses on improving ventricular segmentation in brain MRI images using synthetic data. We employed two latent diffusion models (LDMs): a mask generator trained using 10,000 masks, and a corresponding SPADE image generator optimized using 6,881 scans to create an MRI conditioned on a 3D brain mask. Conditioning the mask generator on ventricular volume in combination with classifier-free guidance enabled the control of the ventricular volume distribution of the generated synthetic images. Next, the performance of the synthetic data was tested using three nnU-Net segmentation models trained on a real, augmented and entirely synthetic data, respectively. The resulting models were tested on a completely independent hold-out dataset of patients with enlarged ventricles, with manual delineation of the ventricles used as ground truth. The model trained on real data showed a mean absolute error (MAE) of 9.09 \pm 12.18 mL in predicted ventricular volume, while the models trained on synthetic and augmented data showed MAEs of 7.52 \pm 4.81 mL and 6.23 \pm 4.33 mL, respectively. Both the synthetic and augmented model also outperformed the state-of-the-art model SynthSeg, which due to limited performance in cases of large ventricular volumes, showed an MAE of 7.73 \pm 12.12 mL with a factor of 3 higher standard deviation. The model trained on augmented data showed the highest Dice score of 0.892 \pm 0.05, slightly outperforming SynthSeg and on par with the model trained on real data. The synthetic model performed similar to SynthSeg. In summary, we provide evidence that guided synthesis of labeled brain MRI data using LDMs improves the segmentation of enlarged ventricles and outperforms existing state-of-the-art segmentation models.

Related papers

• Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net Brain Tumor Segmentation [8.955776982854985]
  We investigate the impact of synthetic MRI data on the robustness and segmentation accuracy of U-Net models for brain tumor segmentation. To quantify the effect of synthetic data contamination, we train U-Net models on progressively "poisoned" datasets.
  arXiv  Detail & Related papers  (2025-02-06T07:21:19Z)
• Embryo 2.0: Merging Synthetic and Real Data for Advanced AI Predictions [69.07284335967019]
  We train two generative models using two datasets, one created and made publicly available, and one existing public dataset. We generate synthetic embryo images at various cell stages, including 2-cell, 4-cell, 8-cell, morula, and blastocyst. These were combined with real images to train classification models for embryo cell stage prediction.
  arXiv  Detail & Related papers  (2024-12-02T08:24:49Z)
• Residual Vision Transformer (ResViT) Based Self-Supervised Learning Model for Brain Tumor Classification [0.08192907805418585]
  Self-supervised learning models provide data-efficient and remarkable solutions to limited dataset problems. This paper introduces a generative SSL model for brain tumor classification in two stages. The proposed model attains the highest accuracy, achieving 90.56% on the BraTs dataset with T1 sequence, 98.53% on the Figshare, and 98.47% on the Kaggle brain tumor datasets.
  arXiv  Detail & Related papers  (2024-11-19T21:42:57Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Towards Synergistic Deep Learning Models for Volumetric Cirrhotic Liver Segmentation in MRIs [1.5228650878164722]
  Liver cirrhosis, a leading cause of global mortality, requires precise segmentation of ROIs for effective disease monitoring and treatment planning. Existing segmentation models often fail to capture complex feature interactions and generalize across diverse datasets. We propose a novel synergistic theory that leverages complementary latent spaces for enhanced feature interaction modeling.
  arXiv  Detail & Related papers  (2024-08-08T14:41:32Z)
• 3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes [2.8498944632323755]
  We propose a novel slice-based latent diffusion architecture to address the complexities of volumetric data generation. This approach extends the joint distribution modeling of medical images and their associated masks, allowing a simultaneous generation of both under data-scarce regimes. Our architecture can be conditioned by tumor characteristics, including size, shape, and relative position, thereby providing a diverse range of tumor variations.
  arXiv  Detail & Related papers  (2024-06-08T09:53:45Z)
• 7T MRI Synthesization from 3T Acquisitions [1.1549572298362787]
  Supervised deep learning techniques can be used to generate synthetic 7T MRIs from 3T MRI inputs. In this paper, we introduce multiple novel 7T synthesization algorithms based on custom-designed variants of the V-Net convolutional neural network.
  arXiv  Detail & Related papers  (2024-03-13T22:06:44Z)
• Federated Learning Enables Big Data for Rare Cancer Boundary Detection [98.5549882883963]
  We present findings from the largest Federated ML study to-date, involving data from 71 healthcare institutions across 6 continents. We generate an automatic tumor boundary detector for the rare disease of glioblastoma. We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent.
  arXiv  Detail & Related papers  (2022-04-22T17:27:00Z)
• Mixed Effects Neural ODE: A Variational Approximation for Analyzing the Dynamics of Panel Data [50.23363975709122]
  We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based Lower Bounds for ME-NODE, and develop (efficient) training algorithms.
  arXiv  Detail & Related papers  (2022-02-18T22:41:51Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)
• Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [59.60954255038335]
  The proposed framework consists of a stretch-out up-sampling module, a brain atlas encoder, a segmentation consistency module, and multi-scale label-wise discriminators. Experiments on real clinical data demonstrate that the proposed model can perform significantly better than the state-of-the-art synthesis methods.
  arXiv  Detail & Related papers  (2020-06-26T02:50:09Z)

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.