TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View

• URL: http://arxiv.org/abs/2502.20637v2
• Date: Thu, 06 Mar 2025 04:31:21 GMT
• Title: TractCloud-FOV: Deep Learning-based Robust Tractography Parcellation in Diffusion MRI with Incomplete Field of View
• Authors: Yuqian Chen, Leo Zekelman, Yui Lo, Suheyla Cetin-Karayumak, Tengfei Xue, Yogesh Rathi, Nikos Makris, Fan Zhang, Weidong Cai, Lauren J. O'Donnell,
• Abstract summary: We introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV.<n>We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios.<n>We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV.
• Score: 7.560473809014894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV. We propose a novel training strategy, FOV-Cut Augmentation (FOV-CA), in which we synthetically cut tractograms to simulate a spectrum of real-world inferior FOV cutoff scenarios. This data augmentation approach enriches the training set with realistic truncated streamlines, enabling the model to achieve superior generalization. We evaluate the proposed TractCloud-FOV on both synthetically cut tractography and two real-life datasets with incomplete FOV. TractCloud-FOV significantly outperforms several state-of-the-art methods on all testing datasets in terms of streamline classification accuracy, generalization ability, tract anatomical depiction, and computational efficiency. Overall, TractCloud-FOV achieves efficient and consistent tractography parcellation in diffusion MRI with incomplete FOV.

Related papers

• Beyond the Lungs: Extending the Field of View in Chest CT with Latent Diffusion Models [15.573780808103985]
  Intermediary between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and effects of lung diseases. Most research chest CT imaging is focused solely on the lungs due to considerations of cost and radiation dose. This restricted field of view poses challenges to comprehensive analysis and hinders the ability to gain insights into the impact of lung diseases on other organs.
  arXiv  Detail & Related papers  (2025-01-22T18:28:18Z)
• Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI [10.096809077954095]
  An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. We propose a novel framework for imputing dMRI scans in the incomplete part of the FOV by integrating the learned diffusion features in the acquired part of the FOV to the complete brain anatomical structure.
  arXiv  Detail & Related papers  (2024-09-20T18:41:29Z)
• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models [8.077960607188261]
  This work aims to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete field-of-view. We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with incomplete FOV. Our approach achieved more accurate whole-brain tractography results with extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's Disease.
  arXiv  Detail & Related papers  (2024-05-06T17:23:42Z)
• CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers [66.15847237150909]
  We introduce a self-supervised deep learning architecture to segment catheters in longitudinal ultrasound images. The network architecture builds upon AiAReSeg, a segmentation transformer built with the Attention in Attention mechanism. We validated our model on a test dataset, consisting of unseen synthetic data and images collected from silicon aorta phantoms.
  arXiv  Detail & Related papers  (2024-03-21T15:13:36Z)
• TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation [63.842881844791094]
  Current tractography parcellation methods rely heavily on registration, but registration inaccuracies can affect parcellation. We propose TractCloud, a registration-free framework that performs whole-brain tractography parcellation directly in individual subject space.
  arXiv  Detail & Related papers  (2023-07-18T06:35:12Z)
• Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions [68.41088365582831]
  White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. Most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient parcellation of 198 SWM clusters from whole-brain tractography.
  arXiv  Detail & Related papers  (2022-07-18T23:07:53Z)
• Body Composition Assessment with Limited Field-of-view Computed Tomography: A Semantic Image Extension Perspective [5.373119949253442]
  Field-of-view (FOV) tissue truncation beyond the lungs is common in routine lung screening computed tomography (CT) In this work, we formulate the problem from the semantic image extension perspective which only requires image data as inputs. The proposed two-stage method identifies a new FOV border based on the estimated extent of the complete body and imputes missing tissues in the truncated region.
  arXiv  Detail & Related papers  (2022-07-13T23:19:22Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images [152.34988415258988]
  Automated detection of lung infections from computed tomography (CT) images offers a great potential to augment the traditional healthcare strategy for tackling COVID-19. segmenting infected regions from CT slices faces several challenges, including high variation in infection characteristics, and low intensity contrast between infections and normal tissues. To address these challenges, a novel COVID-19 Deep Lung Infection Network (Inf-Net) is proposed to automatically identify infected regions from chest CT slices.
  arXiv  Detail & Related papers  (2020-04-22T07:30:56Z)

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.