Hal will be stopped for maintenance from friday on june 10 at 4pm until monday june 13 at 9am. More information
Skip to Main content Skip to Navigation
Journal articles

pBrain: A novel pipeline for Parkinson related brain structure segmentation

Abstract : 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.
Document type :
Journal articles
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02481502
Contributor : Pierrick Coupé Connect in order to contact the contributor
Submitted on : Monday, February 17, 2020 - 3:01:03 PM
Last modification on : Tuesday, January 4, 2022 - 6:17:18 AM
Long-term archiving on: : Monday, May 18, 2020 - 4:57:49 PM

File

pBrain_final_revised.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

José Manjón, Alexa Bertó, José Romero, Enrique Lanuza, Roberto Vivo-Hernando, et al.. pBrain: A novel pipeline for Parkinson related brain structure segmentation. Neuroimage-Clinical, Elsevier, 2020, 25, pp.102184. ⟨10.1016/j.nicl.2020.102184⟩. ⟨hal-02481502⟩

Share

Metrics

Record views

54

Files downloads

35