Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models

• URL: http://arxiv.org/abs/2409.11011v1
• Date: Tue, 17 Sep 2024 09:21:19 GMT
• Title: Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models
• Authors: Emile Saillard, Aurélie Levillain, David Mitton, Jean-Baptiste Pialat, Cyrille Confavreux, Hélène Follet, Thomas Grenier,
• Abstract summary: We propose an automated data pipeline using 3D Denoising Diffusion Probabilistic Models (DDPM) to generalize on new images. We created 5675 new volumes, then trained 3D U-Net segmentation models on real and synthetic data to compare segmentation performance.
• Score: 0.06700983301090582
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Purpose: Bone metastasis have a major impact on the quality of life of patients and they are diverse in terms of size and location, making their segmentation complex. Manual segmentation is time-consuming, and expert segmentations are subject to operator variability, which makes obtaining accurate and reproducible segmentations of bone metastasis on CT-scans a challenging yet important task to achieve. Materials and Methods: Deep learning methods tackle segmentation tasks efficiently but require large datasets along with expert manual segmentations to generalize on new images. We propose an automated data synthesis pipeline using 3D Denoising Diffusion Probabilistic Models (DDPM) to enchance the segmentation of femoral metastasis from CT-scan volumes of patients. We used 29 existing lesions along with 26 healthy femurs to create new realistic synthetic metastatic images, and trained a DDPM to improve the diversity and realism of the simulated volumes. We also investigated the operator variability on manual segmentation. Results: We created 5675 new volumes, then trained 3D U-Net segmentation models on real and synthetic data to compare segmentation performance, and we evaluated the performance of the models depending on the amount of synthetic data used in training. Conclusion: Our results showed that segmentation models trained with synthetic data outperformed those trained on real volumes only, and that those models perform especially well when considering operator variability.

Related papers

• 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)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• 3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation [52.699139151447945]
  We propose a novel adaptation method for transferring the segment anything model (SAM) from 2D to 3D for promptable medical image segmentation. Our model can outperform domain state-of-the-art medical image segmentation models on 3 out of 4 tasks, specifically by 8.25%, 29.87%, and 10.11% for kidney tumor, pancreas tumor, colon cancer segmentation, and achieve similar performance for liver tumor segmentation.
  arXiv  Detail & Related papers  (2023-06-23T12:09:52Z)
• The impact of training dataset size and ensemble inference strategies on head and neck auto-segmentation [0.0]
  Convolutional neural networks (CNNs) are increasingly being used to automate segmentation of organs-at-risk in radiotherapy. We investigated how much data is required to train accurate and robust head and neck auto-segmentation models. An established 3D CNN was trained from scratch with different sized datasets (25-1000 scans) to segment the brainstem, parotid glands and spinal cord in CTs. We evaluated multiple ensemble techniques to improve the performance of these models.
  arXiv  Detail & Related papers  (2023-03-30T12:14:07Z)
• Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation [41.608617301275935]
  We propose a novel end-to-end framework, called Diff-UNet, for medical volumetric segmentation. Our approach integrates the diffusion model into a standard U-shaped architecture to extract semantic information from the input volume effectively. We evaluate our method on three datasets, including multimodal brain tumors in MRI, liver tumors, and multi-organ CT volumes.
  arXiv  Detail & Related papers  (2023-03-18T04:06:18Z)
• Reliable Joint Segmentation of Retinal Edema Lesions in OCT Images [55.83984261827332]
  In this paper, we propose a novel reliable multi-scale wavelet-enhanced transformer network. We develop a novel segmentation backbone that integrates a wavelet-enhanced feature extractor network and a multi-scale transformer module. Our proposed method achieves better segmentation accuracy with a high degree of reliability as compared to other state-of-the-art segmentation approaches.
  arXiv  Detail & Related papers  (2022-12-01T07:32:56Z)
• FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain [0.8376091455761259]
  A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions. The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations. The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
  arXiv  Detail & Related papers  (2022-08-30T16:06:07Z)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Implanting Synthetic Lesions for Improving Liver Lesion Segmentation in CT Exams [0.0]
  We present a method for implanting realistic lesions in CT slices to provide a rich and controllable set of training samples. We conclude that increasing the variability of lesions synthetically in terms of size, density, shape, and position seems to improve the performance of segmentation models for liver lesion segmentation in CT slices.
  arXiv  Detail & Related papers  (2020-08-11T13:23:04Z)
• Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging [2.1204495827342438]
  This manuscript aims to implement a novel model that can learn robust representations from cross-domain data by encapsulating distinct and shared patterns from different modalities. The tests on CT and MRI liver data acquired in routine clinical trials show that the proposed model outperforms all other baseline with a large margin.
  arXiv  Detail & Related papers  (2020-06-08T07:35:55Z)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)

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.