A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI

Cheng, Myra; Galimzianova, Alfiia; Lesjak, Žiga; Špiclin, Žiga; Lock, Christopher; Rubin, Daniel L.

doi:10.1007/978-3-030-00889-5_40

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Dufresne et al ( 2022 ) presented an algorithm that concurrently optimized image registration and local intensity change detection within FLAIR volumes. Cheng et al ( 2018 ) computed lesion changes utilizing T1, T2, and FLAIR sequences. Their approach involved estimating a dissimilarity map between two visits and subsequently incorporating logistic regression with neighborhood information and local texture descriptors.…”

Section: Discussionmentioning

confidence: 99%

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis

Raj,

Gass,

Eisele

et al. 2024

Front. Neurosci.

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IntroductionMultiple sclerosis (MS) is a chronic neurological disorder characterized by the progressive loss of myelin and axonal structures in the central nervous system. Accurate detection and monitoring of MS-related changes in brain structures are crucial for disease management and treatment evaluation. We propose a deep learning algorithm for creating Voxel-Guided Morphometry (VGM) maps from longitudinal MRI brain volumes for analyzing MS disease activity. Our approach focuses on developing a generalizable model that can effectively be applied to unseen datasets.MethodsLongitudinal MS patient high-resolution 3D T1-weighted follow-up imaging from three different MRI systems were analyzed. We employed a 3D residual U-Net architecture with attention mechanisms. The U-Net serves as the backbone, enabling spatial feature extraction from MRI volumes. Attention mechanisms are integrated to enhance the model's ability to capture relevant information and highlight salient regions. Furthermore, we incorporate image normalization by histogram matching and resampling techniques to improve the networks' ability to generalize to unseen datasets from different MRI systems across imaging centers. This ensures robust performance across diverse data sources.ResultsNumerous experiments were conducted using a dataset of 71 longitudinal MRI brain volumes of MS patients. Our approach demonstrated a significant improvement of 4.3% in mean absolute error (MAE) against the state-of-the-art (SOTA) method. Furthermore, the algorithm's generalizability was evaluated on two unseen datasets (n = 116) with an average improvement of 4.2% in MAE over the SOTA approach.DiscussionResults confirm that the proposed approach is fast and robust and has the potential for broader clinical applicability.

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Section: Discussionmentioning

confidence: 99%

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis

Raj,

Gass,

Eisele

et al. 2024

Front. Neurosci.

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“…Also based on DeeplabV3+, a DeeplabV3+ model with multi-scale inputs is proposed to improve image recognition and segmentation performance of cancerous areas in pathological sections of gastric cancer ( Wang and Liu, 2021 ). By incorporating a unique nested jumping device in U-Net to generate semantically similar feature maps in the connected section, a model called U-Net++ is proposed ( Cheng et al, 2018 ). By comparing the segmentation performance of a set of the most representative models (Deeplabv3+, U-Net and U-Net++) for the bull’s-eye region in ultrasound images.…”

Section: Related Workmentioning

confidence: 99%

An efficient convolutional neural network-based diagnosis system for citrus fruit diseases

Huang,

Jiang,

Huang

et al. 2023

Front. Genet.

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Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance.Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network.Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease.Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.

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“…Ganiler et al ( 2014 ) used image subtraction and automated thresholding. Cheng et al ( 2018 ) integrated neighborhood texture in a machine learning framework. Salem et al ( 2018 ) trained a logistic regression model with features from the image intensities, the image subtraction values, and the deformation field operators.…”

Section: Introductionmentioning

confidence: 99%

Longitudinal detection of new MS lesions using deep learning

Kamraoui¹,

Mansencal²,

Manjón³

et al. 2022

Front. Neuroimaging

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The detection of new multiple sclerosis (MS) lesions is an important marker of the evolution of the disease. The applicability of learning-based methods could automate this task efficiently. However, the lack of annotated longitudinal data with new-appearing lesions is a limiting factor for the training of robust and generalizing models. In this study, we describe a deep-learning-based pipeline addressing the challenging task of detecting and segmenting new MS lesions. First, we propose to use transfer-learning from a model trained on a segmentation task using single time-points. Therefore, we exploit knowledge from an easier task and for which more annotated datasets are available. Second, we propose a data synthesis strategy to generate realistic longitudinal time-points with new lesions using single time-point scans. In this way, we pretrain our detection model on large synthetic annotated datasets. Finally, we use a data-augmentation technique designed to simulate data diversity in MRI. By doing that, we increase the size of the available small annotated longitudinal datasets. Our ablation study showed that each contribution lead to an enhancement of the segmentation accuracy. Using the proposed pipeline, we obtained the best score for the segmentation and the detection of new MS lesions in the MSSEG2 MICCAI challenge.

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A Multi-scale Multiple Sclerosis Lesion Change Detection in a Multi-sequence MRI

Cited by 5 publications

References 10 publications

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis

An efficient convolutional neural network-based diagnosis system for citrus fruit diseases

Longitudinal detection of new MS lesions using deep learning

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