| Autor: |
José V. Manjón, Alexa Bertó, José E. Romero, Enrique Lanuza, Roberto Vivo-Hernando, Fernando Aparici-Robles, Pierrick Coupe |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
NeuroImage: Clinical, Vol 25, Iss , Pp - (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2213-1582 |
| DOI: |
10.1016/j.nicl.2020.102184 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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