Abstract
Background: Hippocampus segmentation on magnetic resonance imaging is of key importance for the diagnosis, treatment decision and investigation of neuropsychiatric disorders. Automatic segmentation is an active research field, with many recent models using deep learning. Most current state-of-the art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. This raises the question whether these methods are capable of recognizing the hippocampus on a different domain, that of epilepsy patients with hippocampus resection.New Method: In this paper we present a state-of-the-art, open source, ready-to-use, deep learning based hippocampus segmentation method. It uses an extended 2D multi-orientation approach, with automatic pre-processing and orientation alignment. The methodology was developed and validated using HarP, a public Alzheimer's disease hippocampus segmentation dataset.Results and Comparisons: We test this methodology alongside other recent deep learning methods, in two domains: The HarP test set and an in-house epilepsy dataset, containing hippocampus resections, named HCUnicamp. We show that our method, while trained only in HarP, surpasses others from the literature in both the HarP test set and HCUnicamp in Dice. Additionally, Results from training and testing in HCUnicamp volumes are also reported separately, alongside comparisons between training and testing in epilepsy and Alzheimer's data and vice versa.Conclusion: Although current state-of-the-art methods, including our own, achieve upwards of 0.9 Dice in HarP, all tested methods, including our own, produced false positives in HCUnicamp resection regions, showing that there is still room for improvement for hippocampus segmentation methods when resection is involved.
Highlights
The hippocampus is a small, medial, subcortical brain structure related to long and short term memory [1]
This paper presents a hippocampus segmentation method including consensus of multiple U-Net based Convolutional Neural Networks (CNN) and traditional postprocessing, successfully using a new optimizer and loss function from the literature
The presented method achieves state-of-the-art performance on the public HarP hippocampus segmentation benchmark
Summary
The hippocampus is a small, medial, subcortical brain structure related to long and short term memory [1]. Many of these methods rely on publicly available datasets for training and evaluating and have access only to healthy scans, or patients with Alzheimer’s disease This raises the concern that automated methods might only be prepared to deal with features present in the public Alzheimer’s and healthy subjects datasets, such as ADNI and the Multi Atlas Labeling Challenge (MALC). Considering these facts, we present an improved version of our own deep learning based hippocampus segmentation method [25], compared with other recent methods [7, 8, 9]. Without comparing to other methods, we report results of involving HCUnicamp epilepsy volumes in training
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