A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

• URL: http://arxiv.org/abs/2008.05117v2
• Date: Tue, 15 Sep 2020 13:03:48 GMT
• Title: A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
• Authors: Stefano Cerri, Andrew Hoopes, Douglas N. Greve, Mark M\"uhlau, Koen Van Leemput
• Abstract summary: The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.
• Score: 0.17999333451993946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon.

Related papers

• Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting [2.0168790328644697]
  We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. We achieve superior scores in lesion segmentation as well as lesion detection as compared to state-of-the-art longitudinal and single timepoint models across two datasets.
  arXiv  Detail & Related papers  (2024-09-20T11:30:54Z)
• Accelerating Longitudinal MRI using Prior Informed Latent Diffusion [2.353466020397348]
  We propose a prior informed reconstruction method with a trained diffusion model in conjunction with data-consistency steps. Our method can be trained with unlabeled image data, eliminating the need for a dataset of either k-space measurements or paired longitudinal scans.
  arXiv  Detail & Related papers  (2024-06-29T22:13:54Z)
• Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods [55.07297021627281]
  The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health.
  arXiv  Detail & Related papers  (2023-06-09T02:05:40Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation [0.15833270109954134]
  We build upon an existing whole-brain segmentation method that can handle multi-contrast data and robustly analyze images with white matter lesions. This method is here extended with subject-specific latent variables that encourage temporal consistency between its segmentation results. We validate the proposed method on multiple datasets of control subjects and patients suffering from Alzheimer's disease and multiple sclerosis.
  arXiv  Detail & Related papers  (2022-07-10T20:42:12Z)
• SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae [54.751251046196494]
  We propose a one-stage detection framework termed SpineOne to simultaneously localize and classify degenerative discs and vertebrae from MRI slices. SpineOne is built upon the following three key techniques: 1) a new design of the keypoint heatmap to facilitate simultaneous keypoint localization and classification; 2) the use of attention modules to better differentiate the representations between discs and vertebrae; and 3) a novel gradient-guided objective association mechanism to associate multiple learning objectives at the later training stage.
  arXiv  Detail & Related papers  (2021-10-28T12:59:06Z)
• Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation [4.262342157729123]
  Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. Currently, the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. We propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images.
  arXiv  Detail & Related papers  (2021-02-24T04:09:39Z)
• A Convolutional Approach to Vertebrae Detection and Labelling in Whole Spine MRI [70.04389979779195]
  We propose a novel convolutional method for the detection and identification of vertebrae in whole spine MRIs. This involves using a learnt vector field to group detected vertebrae corners together into individual vertebral bodies. We demonstrate the clinical applicability of this method, using it for automated scoliosis detection in both lumbar and whole spine MR scans.
  arXiv  Detail & Related papers  (2020-07-06T09:37:12Z)
• A Novel Approach for Correcting Multiple Discrete Rigid In-Plane Motions Artefacts in MRI Scans [63.28835187934139]
  We propose a novel method for removing motion artefacts using a deep neural network with two input branches. The proposed method can be applied to artefacts generated by multiple movements of the patient.
  arXiv  Detail & Related papers  (2020-06-24T15:25:11Z)
• 4D Deep Learning for Multiple Sclerosis Lesion Activity Segmentation [49.32653090178743]
  We investigate whether extending this problem to full 4D deep learning using a history of MRI volumes can improve performance. We find that our proposed architecture outperforms previous approaches with a lesion-wise true positive rate of 0.84 at a lesion-wise false positive rate of 0.19.
  arXiv  Detail & Related papers  (2020-04-20T11:41:01Z)

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.