Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Sackl, M; Tinauer, C; Urschler, M; Enzinger, C; Stollberger, R; Ropele, S.
Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks.
Neuroimage. 2024; 298: 120767
Doi: 10.1016/j.neuroimage.2024.120767
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Ropele Stefan
- Sackl Maximilian
- Co-Autor*innen der Med Uni Graz
- Enzinger Christian
- Stollberger Rudolf
- Tinauer Christian Gerhard
- Urschler Martin
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- Abstract:
- Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine. We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures. We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data. In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.
- Find related publications in this database (using NLM MeSH Indexing)
- Humans - administration & dosage
- Hippocampus - diagnostic imaging, pathology
- Magnetic Resonance Imaging - methods
- Deep Learning - administration & dosage
- Image Processing, Computer-Assisted - methods
- Neural Networks, Computer - administration & dosage
- Male - administration & dosage
- Female - administration & dosage
- Aged - administration & dosage
- Alzheimer Disease - diagnostic imaging, pathology
- Neuroimaging - methods, standards
- Find related publications in this database (Keywords)
- High-resolution
- T2-weighted
- CNN
- Segmentation
- Hippocampus atrophy
- FreeSurfer