Multiple Sclerosis Lesions Identification/Segmentation in Magnetic Resonance Imaging using Ensemble CNN and Uncertainty Classification

• URL: http://arxiv.org/abs/2108.11791v1
• Date: Thu, 26 Aug 2021 13:48:06 GMT
• Title: Multiple Sclerosis Lesions Identification/Segmentation in Magnetic Resonance Imaging using Ensemble CNN and Uncertainty Classification
• Authors: Giuseppe Placidi, Luigi Cinque, Filippo Mignosi, Matteo Polsinelli
• Abstract summary: We present an automated framework for MS lesions identification/segmentation based on three pivotal concepts. The proposed framework is trained, validated and tested on the 2016 MSSEG benchmark public data set. Results are also shown for the uncertainty, though a comparison with the other raters is impossible.
• Score: 7.260554897161948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To date, several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions by Magnetic Resonance Imaging (MRI) have been presented which are either outperformed by human experts or, at least, whose results are well distinguishable from humans. This is due to the ambiguity originated by MRI instabilities, peculiar MS Heterogeneity and MRI unspecific nature with respect to MS. Physicians partially treat the uncertainty generated by ambiguity relying on personal radiological/clinical/anatomical background and experience. We present an automated framework for MS lesions identification/segmentation based on three pivotal concepts to better emulate human reasoning: the modeling of uncertainty; the proposal of two, separately trained, CNN, one optimized with respect to lesions themselves and the other to the environment surrounding lesions, respectively repeated for axial, coronal and sagittal directions; the ensemble of the CNN output. The proposed framework is trained, validated and tested on the 2016 MSSEG benchmark public data set from a single imaging modality, FLuid-Attenuated Inversion Recovery (FLAIR). The comparison, performed on the segmented lesions by means of most of the metrics normally used with respect to the ground-truth and the 7 human raters in MSSEG, prove that there is no significant difference between the proposed framework and the other raters. Results are also shown for the uncertainty, though a comparison with the other raters is impossible.

Related papers

• Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI [1.8420387715849447]
  Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks. Their notable performance heavily relies on labelled datasets, which limits their application in medical images. This paper introduces a novel framework by incorporating distinctive discrepancy features.
  arXiv  Detail & Related papers  (2024-05-08T11:26:49Z)
• QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge [93.61262892578067]
  Uncertainty in medical image segmentation tasks, especially inter-rater variability, presents a significant challenge. This variability directly impacts the development and evaluation of automated segmentation algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical Image Quantification Challenge (QUBIQ)
  arXiv  Detail & Related papers  (2024-03-19T17:57:24Z)
• Classification of Radiologically Isolated Syndrome and Clinically Isolated Syndrome with Machine-Learning Techniques [0.0]
  The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS) Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS.
  arXiv  Detail & Related papers  (2024-01-24T08:49:50Z)
• 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)
• GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis [52.04899592688968]
  We propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images. GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks.
  arXiv  Detail & Related papers  (2023-01-11T11:38:37Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Variational Inference for Quantifying Inter-observer Variability in Segmentation of Anatomical Structures [12.138198227748353]
  Most segmentation methods simply model a mapping from an image to its single segmentation map and do not take the disagreement of annotators into consideration. We propose a novel variational inference framework to model the distribution of plausible segmentation maps, given a specific MR image.
  arXiv  Detail & Related papers  (2022-01-18T16:33:33Z)
• Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images [0.2578242050187029]
  Multiple Sclerosis (MS) is an autoimmune, and demyelinating disease that leads to lesions in the central nervous system. Up to now a multitude of multimodality automatic biomedical approaches is used to segment lesions. Authors propose a method employing just one modality (FLAIR image) to segment MS lesions accurately.
  arXiv  Detail & Related papers  (2022-01-05T21:37:43Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction [63.202627467245584]
  We introduce a Bayesian variational framework to quantify the model-immanent (epistemic) uncertainty. We demonstrate that our approach yields competitive results for undersampled MRI reconstruction.
  arXiv  Detail & Related papers  (2021-02-12T18:08:14Z)
• 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.