Corpus ID: 237502824

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

@article{Liu2021CrossModalityDA,
  title={Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation},
  author={Han Liu and Yubo Fan and Can Cui and Dingjie Su and Andrew McNeil and Benoit M. Dawant},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.06274}
}
Automatic methods to segment the vestibular schwannoma (VS) tumors and the cochlea from magnetic resonance imaging (MRI) are critical to VS treatment planning. Although supervised methods have achieved satisfactory performance in VS segmentation, they require full annotations by experts, which is laborious and time-consuming. In this work, we aim to tackle the VS and cochlea segmentation problem in an unsupervised domain adaptation setting. Our proposed method leverages both the image-level… Expand

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References

SHOWING 1-10 OF 20 REFERENCES
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss
TLDR
This work introduces a 2.5D convolutional neural network able to exploit the different in-plane and through-plane resolutions encountered in standard of care imaging protocols and proposes a hardness-weighted Dice loss function that gives higher weights to harder voxels to boost the training of CNNs. Expand
Segmentation of vestibular schwannoma from MRI - An open annotated dataset and baseline algorithm
TLDR
This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution, providing the first publicly-available annotated imaging dataset of VS. Expand
Unsupervised domain adaptation for medical imaging segmentation with self-ensembling
TLDR
This work extends the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explores multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Expand
Using Bayesian tissue classification to improve the accuracy of vestibular schwannoma volume and growth measurement.
TLDR
Bayesian partial volume segmentation provides a more accurate and rapid method of volume and growth estimation of vestibular schwannomas and allows identification of tumor growth in 10 of 12 cases that appeared to be static in size when manual segmentation techniques are used. Expand
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
TLDR
The proposed sliced Wasserstein discrepancy (SWD) is designed to capture the natural notion of dissimilarity between the outputs of task-specific classifiers and enables efficient distribution alignment in an end-to-end trainable fashion. Expand
Contrastive Learning for Unpaired Image-to-Image Translation
TLDR
The framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time, and can be extended to the training setting where each "domain" is only a single image. Expand
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training
TLDR
This paper proposes a novel UDA framework based on an iterative self-training (ST) procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels. Expand
A reproducible evaluation of ANTs similarity metric performance in brain image registration
TLDR
This is the first study to use a consistent transformation framework to provide a reproducible evaluation of the isolated effect of the similarity metric on optimal template construction and brain labeling, and to quantify the similarity of templates derived from different subgroups. Expand
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
TLDR
A novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model that adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs is proposed. Expand
Domain-Adversarial Training of Neural Networks
TLDR
A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. Expand
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