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RESEARCH PRODUCT
pBrain: A novel pipeline for Parkinson related brain structure segmentation
Enrique LanuzaPierrick CoupéJosé E. RomeroFernando Aparici RoblesJosé V. ManjónRoberto Vivo-hernandoAlexa Bertósubject
AdultMaleComputer scienceCognitive NeurosciencePipeline (computing)NeuroimagingSubstantia nigraImage processinglcsh:Computer applications to medicine. Medical informaticslcsh:RC346-429050105 experimental psychologyNeurologia03 medical and health sciences0302 clinical medicineImage Interpretation Computer-Assisted[INFO.INFO-IM]Computer Science [cs]/Medical ImagingImage Processing Computer-AssistedHumans0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imagingSegmentationlcsh:Neurology. Diseases of the nervous systemAgedStructure (mathematical logic)Artificial neural networkbusiness.industry05 social sciencesBrainReproducibility of ResultsRegular ArticleParkinson DiseasePattern recognitionMiddle AgedMagnetic Resonance ImagingSubthalamic nucleusNeurologyFISICA APLICADAlcsh:R858-859.7Sistema nerviós MalaltiesFemaleNeurology (clinical)Artificial intelligencebusinessError detection and correctionLENGUAJES Y SISTEMAS INFORMATICOS030217 neurology & neurosurgerydescription
[EN] Parkinson is a very prevalent neurodegenerative disease impacting the life of millions of people worldwide. Although its cause remains unknown, its functional and structural analysis is fundamental to advance in the search of a cure or symptomatic treatment. The automatic segmentation of deep brain structures related to Parkinson's disease could be beneficial for the follow up and treatment planning. Unfortunately, there is not broadly available segmentation software to automatically measure Parkinson related structures. In this paper, we present a novel pipeline to segment three deep brain structures related to Parkinson's disease (substantia nigra, subthalamic nucleus and red nucleus). The proposed method is based on the multi-atlas label fusion technology that works on standard and high-resolution T2-weighted images. The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The proposed method has been compared to other state-of-the-art methods showing competitive results in terms of accuracy and execution time.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |