ArticleBrain structure & function2024
Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation.
Article in Brain structure & function, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Enhancing Asleep Deep Brain Stimulation Targeting for Essential Tremor Using Machine Learning: The OPTIVIM Phase 2 Study.Neurosurgery · 2026Trial
- Thalamic Nuclei Atrophy in Type 1 Diabetes Mellitus Across Disease Duration.Journal of magnetic resonance imaging : JMRI · 2026Article
- Volumetric and functional connectivity alterations of the thalamus and thalamic nuclei groups in drug-resistant focal epilepsy: A 7T MRI study.Neuroradiology · 2026Article
- Toward reliable thalamic segmentation: an evaluation of automated methods for structural MRI.Brain structure & function · 2026Article
- Nucleus-level thalamic organization anchors multimodal signatures of thalamocortical maturation.bioRxiv : the preprint server for biology · 2026Article
- Diffusion-Informed Joint Segmentation Enhances Detection of Thalamic Atrophy in Parkinson's Disease.Brain topography · 2026Article
- Diencephalic integrity explains aspects of hippocampal amnesia.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Thalamic nuclei insights into Alzheimer's disease.bioRxiv : the preprint server for biology · 2026Article
- Thalamic nuclei volumes across psychiatric and neurological disorders: a multi-site magnetic resonance imaging study.Translational psychiatry · 2026Article
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- Synthetic multi-inversion time magnetic resonance images for visualization of subcortical structures.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Imaging and procedural features of staged bilateral magnetic resonance-guided focused ultrasound thalamotomy for essential tremor.Frontiers in radiology · 2026Article
- A deep learning framework for comprehensive segmentation of deep grey nuclei.medRxiv : the preprint server for health sciences · 2025Article
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- Atrophy patterns of deep gray matter nuclei in Alzheimer's disease and frontotemporal dementia.Journal of Alzheimer's disease : JAD · 2025Article
- Comprehensive Segmentation of Deep Grey Nuclei From Structural MRI Data.Human brain mapping · 2025Article
- Physiological and brain mechanisms contributing to postural tremor in aging with and without alcohol use disorder.Translational psychiatry · 2025Article
- Evaluating the effective segmentation of human lateral geniculate nucleus.Brain structure & function · 2025Article
- Normative Modeling of Thalamic Nuclear Volumes and Characterization of Lateralized Volume Alterations in Alzheimer's Disease Versus Schizophrenia.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
- Disuse-driven plasticity in the human thalamus and putamen.Cell reports · 2025Article
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Abstract
Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for the use of T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3 T and 7 T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.
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