Towards contrast-agnostic soft segmentation of the spinal cord

• URL: http://arxiv.org/abs/2310.15402v2
• Date: Tue, 23 Jul 2024 12:12:41 GMT
• Title: Towards contrast-agnostic soft segmentation of the spinal cord
• Authors: Sandrine Bédard, Enamundram Naga Karthik, Charidimos Tsagkas, Emanuele Pravatà, Cristina Granziera, Andrew Smith, Kenneth Arnold Weber II, Julien Cohen-Adad,
• Abstract summary: We present a deep learning-based method that produces soft segmentations of the spinal cord. Our model generalizes better than the state-of-the-art methods amongst unseen datasets, vendors, contrasts, and pathologies.
• Score: 0.27029650498548424
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants ($\text{n}=267$; $\text{contrasts}=6$), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with aggressive data augmentation and a regression-based loss function, were used to train a U-Net model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different loss functions and domain generalization methods. Our results show that using the soft segmentations along with a regression loss function reduces CSA variability ($p < 0.05$, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects.

Related papers

• Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
  Medical image segmentation plays a crucial role in computer-aided diagnosis. We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
  arXiv  Detail & Related papers  (2023-12-26T12:56:31Z)
• Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding [51.38395069380457]
  Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proactive measures to mitigate the risk of severe back pain and disability. In this study, we specifically explore the use of shape auto-encoders for vertebrae.
  arXiv  Detail & Related papers  (2023-12-08T18:11:22Z)
• How inter-rater variability relates to aleatoric and epistemic uncertainty: a case study with deep learning-based paraspinal muscle segmentation [1.9624082208594296]
  We study how inter-rater variability affects the reliability of the resulting deep learning algorithms. Our study reveals the interplay between inter-rater variability and uncertainties, affected by choices of label fusion strategies and DL models.
  arXiv  Detail & Related papers  (2023-08-14T06:40:20Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• 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)
• Contrast Adaptive Tissue Classification by Alternating Segmentation and Synthesis [0.21111026813272174]
  We describe an approach using alternating segmentation and synthesis steps that adapts the contrast properties of the training data to the input image. A notable advantage of this approach is that only a single example of the acquisition protocol is required to adapt to its contrast properties.
  arXiv  Detail & Related papers  (2021-03-04T00:25:24Z)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)
• Bone Segmentation in Contrast Enhanced Whole-Body Computed Tomography [2.752817022620644]
  This paper outlines a U-net architecture with novel preprocessing techniques to segment bone-bone marrow regions from low dose contrast enhanced whole-body CT scans. We have demonstrated that appropriate preprocessing is important for differentiating between bone and contrast dye, and that excellent results can be achieved with limited data.
  arXiv  Detail & Related papers  (2020-08-12T10:48:38Z)
• 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)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)
• Spinal Metastases Segmentation in MR Imaging using Deep Convolutional Neural Networks [0.0]
  This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. We used a U-Net like architecture trained with 40 clinical cases including both, lytic and sclerotic lesion types and various MR sequences. Compared to expertly annotated lesion segmentations, the experiments yielded promising results with average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%.
  arXiv  Detail & Related papers  (2020-01-08T10:59:31Z)

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.