A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans

• URL: http://arxiv.org/abs/2505.12999v1
• Date: Mon, 19 May 2025 11:39:18 GMT
• Title: A generalisable head MRI defacing pipeline: Evaluation on 2,566 meningioma scans
• Authors: Lorena Garcia-Foncillas Macias, Aaron Kujawa, Aya Elshalakany, Jonathan Shapey, Tom Vercauteren,
• Abstract summary: We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain masking.<n>Our method was evaluated on 2,566 heterogeneous clinical scans for meningioma and achieved a 99.92 per cent success rate.
• Score: 2.7219200491616378
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable MRI defacing techniques to safeguard patient privacy while preserving brain anatomy are critical for research collaboration. Existing methods often struggle with incomplete defacing or degradation of brain tissue regions. We present a robust, generalisable defacing pipeline for high-resolution MRI that integrates atlas-based registration with brain masking. Our method was evaluated on 2,566 heterogeneous clinical scans for meningioma and achieved a 99.92 per cent success rate (2,564/2,566) upon visual inspection. Excellent anatomical preservation is demonstrated with a Dice similarity coefficient of 0.9975 plus or minus 0.0023 between brain masks automatically extracted from the original and defaced volumes. Source code is available at https://github.com/cai4cai/defacing_pipeline.

Related papers

• Efficient Brain Extraction of MRI Scans with Mild to Moderate Neuropathology [0.0]
  We propose a novel approach to strip T1-weighted images in a robust and efficient manner.<n>We train a modified version of the U-net on silver-standard ground truth data using a novel loss function based on the signed-distance transform (SDT)<n>Our method achieves performance comparable to or better than existing state-of-the-art methods for brain extraction.
  arXiv  Detail & Related papers  (2026-02-09T15:03:30Z)
• Hide-and-Seek Attribution: Weakly Supervised Segmentation of Vertebral Metastases in CT [68.09387763135236]
  We introduce a weakly supervised method trained solely on vertebra-level healthy/malignant labels, without any lesion masks.<n>We achieve strong blastic/lytic performance despite no mask supervision.
  arXiv  Detail & Related papers  (2025-12-07T14:03:28Z)
• TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood [1.1144337101638346]
  We present TissUnet, a deep learning model that segments skull bone, subcutaneous fat, and muscle from routine three-dimensional T1-weighted MRI.<n>The model was trained on 155 paired MRI- computed tomography (CT) scans and validated across nine datasets.
  arXiv  Detail & Related papers  (2025-06-06T01:21:34Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• A deep learning approach for brain tumor detection using magnetic resonance imaging [0.0]
  Brain tumors are considered one of the most dangerous disorders in children and adults. A convolution neural network (CNN)-based illustration has been proposed for detecting brain tumors from MRI images. The proposed model has achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate.
  arXiv  Detail & Related papers  (2022-10-25T10:13:29Z)
• Are Macula or Optic Nerve Head Structures better at Diagnosing Glaucoma? An Answer using AI and Wide-Field Optical Coherence Tomography [48.7576911714538]
  We developed a deep learning algorithm to automatically segment structures of the optic nerve head (ONH) and macula in 3D wide-field OCT scans. Our classification algorithm was able to segment ONH and macular tissues with a DC of 0.94 $pm$ 0.003. This may encourage the mainstream adoption of 3D wide-field OCT scans.
  arXiv  Detail & Related papers  (2022-10-13T01:51:29Z)
• Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine [72.85011943179894]
  We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched.
  arXiv  Detail & Related papers  (2022-05-16T10:59:55Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor Analysis using MRI [0.28675177318965034]
  Two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs) In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. In the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
  arXiv  Detail & Related papers  (2022-01-14T10:24:47Z)
• Symmetry-Enhanced Attention Network for Acute Ischemic Infarct Segmentation with Non-Contrast CT Images [50.55978219682419]
  We propose a symmetry enhanced attention network (SEAN) for acute ischemic infarct segmentation. Our proposed network automatically transforms an input CT image into the standard space where the brain tissue is bilaterally symmetric. The proposed SEAN outperforms some symmetry-based state-of-the-art methods in terms of both dice coefficient and infarct localization.
  arXiv  Detail & Related papers  (2021-10-11T07:13:26Z)
• Rapid head-pose detection for automated slice prescription of fetal-brain MRI [2.0526610003396657]
  In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views. We propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take 5 seconds to acquire. The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%.
  arXiv  Detail & Related papers  (2021-10-08T13:59:05Z)
• Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images [0.0]
  Our work involves implementing a deep convolutional neural network (DCNN) for diagnosing brain tumors from MR images. Our model can single out the MR images with tumors with an overall accuracy of 96%.
  arXiv  Detail & Related papers  (2020-10-03T07:42:17Z)
• 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)
• Robust Automatic Whole Brain Extraction on Magnetic Resonance Imaging of Brain Tumor Patients using Dense-Vnet [5.684776869811468]
  Whole brain extraction, also known as skull stripping, is a process in neuroimaging in which non-brain tissue such as skull, eyeballs, skin, etc. are removed from neuroimages. Despite a plethora of skull stripping approaches in the literature, few are sufficiently accurate for processing pathology-presenting MRIs, especially MRIs with brain tumors. We propose a deep learning approach for skull striping common MRI sequences in oncology such as T1-weighted with gadolinium contrast (T1Gd) and T2-weighted fluid attenuated inversion recovery (FLAIR) in patients with brain tumors.
  arXiv  Detail & Related papers  (2020-06-04T03:18:43Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.