Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

• URL: http://arxiv.org/abs/2209.02032v1
• Date: Mon, 5 Sep 2022 16:09:24 GMT
• Title: Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets
• Authors: Benjamin Billot, Colin Magdamo, Steven E. Arnold, Sudeshna Das, Juan. E. Iglesias
• Abstract summary: We present SynthSeg+, an AI segmentation suite that enables robust analysis of heterogeneous clinical datasets. Specifically, in addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations. We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality.
• Score: 1.0499611180329802
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm can cope with the high variability in clinical acquisitions (MR contrast, resolution, orientation, etc.). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. Specifically, in addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry in wide-ranging settings.

Related papers

• An Ensemble Approach for Brain Tumor Segmentation and Synthesis [0.12777007405746044]
  The integration of machine learning in magnetic resonance imaging (MRI) is proving to be incredibly effective. Deep learning models utilize multiple layers of processing to capture intricate details of complex data. We propose a deep learning framework that ensembles state-of-the-art architectures to achieve accurate segmentation.
  arXiv  Detail & Related papers  (2024-11-26T17:28:51Z)
• SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale from Ultra-High Resolution 7T Magnetic Resonance Angiograms [60.35639972035727]
  The lack of publicly available annotated datasets has impeded the development of robust, machine learning-driven segmentation algorithms. The SMILE-UHURA challenge addresses the gap in publicly available annotated datasets by providing an annotated dataset of Time-of-Flight angiography acquired with 7T MRI. Dice scores reached up to 0.838 $pm$ 0.066 and 0.716 $pm$ 0.125 on the respective datasets, with an average performance of up to 0.804 $pm$ 0.15.
  arXiv  Detail & Related papers  (2024-11-14T17:06:00Z)
• Recon-all-clinical: Cortical surface reconstruction and analysis of heterogeneous clinical brain MRI [3.639043225506316]
  We introduce recon-all-clinical, a novel method for cortical reconstruction, registration, parcellation, and thickness estimation in brain MRI scans. Our approach employs a hybrid analysis method that combines a convolutional neural network (CNN) trained with domain randomization to predict signed distance functions. We tested recon-all-clinical on multiple datasets, including over 19,000 clinical scans.
  arXiv  Detail & Related papers  (2024-09-05T19:52:09Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Cortical analysis of heterogeneous clinical brain MRI scans for large-scale neuroimaging studies [2.930354460501359]
  Surface analysis of the cortex is ubiquitous in human neuroimaging with MRI, e.g., for cortical registration, parcellation, or thickness estimation. Here we present the first method for cortical reconstruction, registration, parcellation, and thickness estimation for clinical brain MRI scans of any resolution and pulse sequence.
  arXiv  Detail & Related papers  (2023-05-02T23:36:06Z)
• Robust Segmentation of Brain MRI in the Wild with Hierarchical CNNs and no Retraining [1.0499611180329802]
  Retrospective analysis of brain MRI scans acquired in the clinic has the potential to enable neuroimaging studies with sample sizes much larger than those found in research datasets. Recent advances in convolutional neural networks (CNNs) and domain randomisation for image segmentation may enable morphometry of clinical MRI at scale. We show that SynthSeg is generally robust, but frequently falters on scans with low signal-to-noise ratio or poor tissue contrast. We propose SynthSeg+, a novel method that greatly mitigates these problems using a hierarchy of conditional segmentation and denoising CNNs.
  arXiv  Detail & Related papers  (2022-03-03T19:18:28Z)
• SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and Resolution [7.070890465817133]
  Convolutional neural networks (CNNs) have difficulties generalising to unseen target domains. We introduce SynthSeg, the first segmentation CNN to brain MRI scans of any contrast and resolution. We demonstrate SynthSeg on 5,500 scans of 6 modalities and 10 resolutions, where it exhibits unparalleled generalisation.
  arXiv  Detail & Related papers  (2021-07-20T15:22:16Z)
• Towards an Automatic Analysis of CHO-K1 Suspension Growth in Microfluidic Single-cell Cultivation [63.94623495501023]
  We propose a novel Machine Learning architecture, which allows us to infuse a neural deep network with human-powered abstraction on the level of data. Specifically, we train a generative model simultaneously on natural and synthetic data, so that it learns a shared representation, from which a target variable, such as the cell count, can be reliably estimated.
  arXiv  Detail & Related papers  (2020-10-20T08:36:51Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• Modeling Shared Responses in Neuroimaging Studies through MultiView ICA [94.31804763196116]
  Group studies involving large cohorts of subjects are important to draw general conclusions about brain functional organization. We propose a novel MultiView Independent Component Analysis model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. We demonstrate the usefulness of our approach first on fMRI data, where our model demonstrates improved sensitivity in identifying common sources among subjects.
  arXiv  Detail & Related papers  (2020-06-11T17:29:53Z)
• 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)

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.