ASCHOPLEX: A generalizable approach for the automatic segmentation of choroid plexus

Abstract

Background: The Choroid Plexus (ChP) plays a vital role in brain homeostasis, serving as part of the Blood-Cerebrospinal Fluid Barrier, contributing to brain clearance pathways and being the main source of cerebrospinal fluid. Since the involvement of ChP in neurological and psychiatric disorders is not entirely established and currently under investigation, accurate and reproducible segmentation of this brain structure on large cohorts remains challenging. This paper presents ASCHOPLEX, a deep-learning tool for the automated segmentation of human ChP from structural MRI data that integrates existing software architectures like 3D UNet, UNETR, and DynUNet to deliver accurate ChP volume estimates.

Methods: Here we trained ASCHOPLEX on 128 T1-w MRI images comprising both controls and patients with Multiple Sclerosis. ASCHOPLEX's performances were evaluated using traditional segmentation metrics; manual segmentation by experts served as ground truth. To overcome the generalizability problem that affects data-driven approaches, an additional fine-tuning procedure (ASCHOPLEX_tune) was implemented on 77 T1-w PET/MRI images of both controls and depressed patients.

Results: ASCHOPLEX showed superior performance compared to commonly used methods like FreeSurfer and Gaussian Mixture Model both in terms of Dice Coefficient (ASCHOPLEX 0.80, ASCHOPLEX_tune 0.78) and estimated ChP volume error (ASCHOPLEX 9.22%, ASCHOPLEX_tune 9.23%).

Conclusion: These results highlight the high accuracy, reliability, and reproducibility of ASCHOPLEX ChP segmentations.

Keywords: Choroid plexus; Deep neural networks; Ensemble; Magnetic resonance imaging; Semantic segmentation.