Dataset Information

Defining the human hippocampus in cerebral magnetic resonance images--an overview of current segmentation protocols.

ABSTRACT: Due to its crucial role for memory processes and its relevance in neurological and psychiatric disorders, the hippocampus has been the focus of neuroimaging research for several decades. In vivo measurement of human hippocampal volume and shape with magnetic resonance imaging has become an important element of neuroimaging research. Nevertheless, volumetric findings are still inconsistent and controversial for many psychiatric conditions including affective disorders. Here we review the wealth of anatomical protocols for the delineation of the hippocampus in MR images, taking into consideration 71 different published protocols from the neuroimaging literature, with an emphasis on studies of affective disorders. We identified large variations between protocols in five major areas. 1) The inclusion/exclusion of hippocampal white matter (alveus and fimbria), 2) the definition of the anterior hippocampal-amygdala border, 3) the definition of the posterior border and the extent to which the hippocampal tail is included, 4) the definition of the inferior medial border of the hippocampus, and 5) the use of varying arbitrary lines. These are major sources of variance between different protocols. In contrast, the definitions of the lateral, superior, and inferior borders are less disputed. Directing resources to replication studies that incorporate characteristics of the segmentation protocols presented herein may help resolve seemingly contradictory volumetric results between prior neuroimaging studies and facilitate the appropriate selection of protocols for manual or automated delineation of the hippocampus for future research purposes.

SUBMITTER: Konrad C

PROVIDER: S-EPMC3299307 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

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Defining the human hippocampus in cerebral magnetic resonance images--an overview of current segmentation protocols.

Konrad C C Ukas T T Nebel C C Arolt V V Toga A W AW Narr K L KL

NeuroImage 20090515 4

Due to its crucial role for memory processes and its relevance in neurological and psychiatric disorders, the hippocampus has been the focus of neuroimaging research for several decades. In vivo measurement of human hippocampal volume and shape with magnetic resonance imaging has become an important element of neuroimaging research. Nevertheless, volumetric findings are still inconsistent and controversial for many psychiatric conditions including affective disorders. Here we review the wealth o ...[more]

PMID: 19447182

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Project description:BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for non-invasive myocardial tissue characterisation. However, accurate segmentation of the left ventricular (LV) myocardium remains a challenge due to limited training data and lack of quality control. This study addresses these issues by leveraging generative adversarial networks (GAN)-generated virtual native enhancement (VNE) images to expand the training set and incorporating an automated quality control-driven (QCD) framework to improve segmentation reliability.MethodsA dataset comprising 4,716 LGE images (from 1,363 patients with hypertrophic cardiomyopathy and myocardial infarction) was used for development. To generate additional clinically validated data, LGE data were augmented with a GAN-based generator to produce VNE images. LV was contoured on these images manually by clinical observers. To create diverse candidate segmentations, the QCD framework involved multiple U-Nets, which were combined using statistical rank filters. The framework predicted the Dice Similarity Coefficient (DSC) for each candidate segmentation, with the highest predicted DSC indicating the most accurate and reliable result. The performance of the QCD ensemble framework was evaluated on both LGE and VNE test datasets (309 LGE/VNE images from 103 patients), assessing segmentation accuracy (DSC) and quality prediction (mean absolute error (MAE) and binary classification accuracy).ResultsThe QCD framework effectively and rapidly segmented the LV myocardium (<1 s per image) on both LGE and VNE images, demonstrating robust performance on both test datasets with similar mean DSC (LGE: 0.845±0.075; VNE: 0.845±0.071; p=ns). Incorporating GAN-generated VNE data into the training process consistently led to enhanced performance for both individual models and the overall framework. The quality control mechanism yielded a high performance (MAE=0.043, accuracy=0.951) emphasising the accuracy of the quality control-driven strategy in predicting segmentation quality in clinical settings. Overall, no statistical difference (p=ns) was found when comparing the LGE and VNE test sets across all experiments.ConclusionsThe QCD ensemble framework, leveraging GAN-generated VNE data and an automated quality control mechanism, significantly improved the accuracy and reliability of LGE segmentation, paving the way for enhanced and accountable diagnostic imaging in routine clinical use.

Dataset Information

Defining the human hippocampus in cerebral magnetic resonance images--an overview of current segmentation protocols.

Publications

Defining the human hippocampus in cerebral magnetic resonance images--an overview of current segmentation protocols.

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