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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.
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https://hal.archives-ouvertes.fr/hal-02481502
Contributor : Pierrick Coupé <>
Submitted on : Monday, February 17, 2020 - 3:01:03 PM
Last modification on : Thursday, March 12, 2020 - 3:06:05 PM

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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⟩

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