2 research outputs found
Comparative Evaluation Of Three Methods Of Automatic Segmentation Of Brain Structures Using 426 Cases
Segmentation of brain structures in a large dataset of magnetic resonance
images (MRI) necessitates automatic segmentation instead of manual tracing.
Automatic segmentation methods provide a much-needed alternative to manual
segmentation which is both labor intensive and time-consuming. Among brain
structures, the hippocampus presents a challenging segmentation task due to its
irregular shape, small size, and unclear edges. In this work, we use
T1-weighted MRI of 426 subjects to validate the approach and compare three
automatic segmentation methods: FreeSurfer, LocalInfo, and ABSS. Four
evaluation measures are used to assess agreement between automatic and manual
segmentation of the hippocampus. ABSS outperformed the others based on the Dice
coefficient, precision, Hausdorff distance, ASSD, RMS, similarity, sensitivity,
and volume agreement. Moreover, comparison of the segmentation results,
acquired using 1.5T and 3T MRI systems, showed that ABSS is more sensitive than
the others to the field inhomogeneity of 3T MRI
Hippocampus Segmentation on Epilepsy and Alzheimer's Disease Studies with Multiple Convolutional Neural Networks
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. 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. 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. 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.Comment: Code is available at https://github.com/dscarmo/e2dhipseg Published
in Heliyon:
https://www.sciencedirect.com/science/article/pii/S240584402100331