ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2019
Multiple Sclerosis Lesion Segmentation with Tiramisu and 2.5D Stacked Slices.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Review of Deep Learning Approaches for the Segmentation of Multiple Sclerosis Lesions on Brain MRI.Frontiers in neuroinformatics · 2020Pooled it
- Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.Insights into imaging · 2026Article
- UNISELF: A unified network with instance normalization and self-ensembled lesion fusion for multiple sclerosis lesion segmentation.Medical image analysis · 2026Article
- Instance-level quantitative saliency in multiple sclerosis lesion segmentation.Scientific reports · 2026Article
- White matter hyperintensity segmentation of multiple sclerosis and neuromyelitis optical spectrum disorders using 2.5D FrC-ResUnet.Quantitative imaging in medicine and surgery · 2025Article
- Article
- Precise diagnosis of pediatric posterior cranial fossa neoplasms based on 2.5D MRI deep learning.Frontiers in oncology · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Quantitative susceptibility mapping based basal ganglia segmentation via AGSeg: leveraging active gradient guiding mechanism in deep learning.Quantitative imaging in medicine and surgery · 2024Article
- Automatic hepatic tumor segmentation in intra-operative ultrasound: a supervised deep-learning approach.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI.Neuroimage. Reports · 2024Article
- 2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.Quantitative imaging in medicine and surgery · 2024Article
- A joint model for lesion segmentation and classification of MS and NMOSD.Frontiers in neuroscience · 2024Article
- CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision · 2024Article
- Investigation of an efficient multi-modal convolutional neural network for multiple sclerosis lesion detection.Scientific reports · 2023Article
- Learning from multiple annotators for medical image segmentation.Pattern recognition · 2023Article
- Multi-arm U-Net with dense input and skip connectivity for T2 lesion segmentation in clinical trials of multiple sclerosis.Scientific reports · 2023Article
- CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.IEEE transactions on medical imaging · 2023Article
- HC-Net: A hybrid convolutional network for non-human primate brain extraction.Frontiers in computational neuroscience · 2023Article
- Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review.Neuroradiology · 2022Review
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Authors and funding
8 authors.
Funding
Abstract
In this paper, we present a fully convolutional densely connected network (Tiramisu) for multiple sclerosis (MS) lesion segmentation. Different from existing methods, we use stacked slices from all three anatomical planes to achieve a 2.5D method. Individual slices from a given orientation provide global context along the plane and the stack of adjacent slices adds local context. By taking stacked data from three orientations, the network has access to more samples for training and can make more accurate segmentation by combining information of different forms. The conducted experiments demonstrated the competitive performance of our method. For an ablation study, we simulated lesions on healthy controls to generate images with ground truth lesion masks. This experiment confirmed that the use of 2.5D patches, stacked data and the Tiramisu model improve the MS lesion segmentation performance. In addition, we evaluated our approach on the Longitudinal MS Lesion Segmentation Challenge. The overall score of 93.1 places the
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.