DEEP SIMILARITY LEARNING FOR MULTIMODAL MEDICAL IMAGES
First Claim
1. A method for similarity metric learning for multimodal medical image data, the method comprising:
- receiving a first set of image data of a volume, wherein the first set of image data is captured with a first imaging modality;
receiving a second set of image data of the volume, wherein the second set of image data is captured with a second imaging modality;
aligning the first set of image data and the second set of image data;
training a first set of parameters with a multimodal stacked denoising auto encoder to generate a shared feature representation of the first set of image data and the second set of image data;
training a second set of parameters with a denoising auto encoder to generate a transformation of the shared feature representation;
initializing, using the first set of parameters and the second set of parameters, a neural network classifier; and
training, using training data from the aligned first set of image data and the second set of image data, the neural network classifier to generate a similarity metric for the first and second imaging modalities.
3 Assignments
0 Petitions
Accused Products
Abstract
The present embodiments relate to machine learning for multimodal image data. By way of introduction, the present embodiments described below include apparatuses and methods for learning a similarity metric using deep learning based techniques for multimodal medical images. A novel similarity metric for multi-modal images is provided using the corresponding states of pairs of image patches to generate a classification setting for each pair. The classification settings are used to train a deep neural network via supervised learning. A multi-modal stacked denoising auto encoder (SDAE) is used to pre-train the neural network. A continuous and smooth similarity metric is constructed based on the output of the neural network before activation in the last layer. The trained similarity metric may be used to improve the results of image fusion.
60 Citations
20 Claims
-
1. A method for similarity metric learning for multimodal medical image data, the method comprising:
-
receiving a first set of image data of a volume, wherein the first set of image data is captured with a first imaging modality; receiving a second set of image data of the volume, wherein the second set of image data is captured with a second imaging modality; aligning the first set of image data and the second set of image data; training a first set of parameters with a multimodal stacked denoising auto encoder to generate a shared feature representation of the first set of image data and the second set of image data; training a second set of parameters with a denoising auto encoder to generate a transformation of the shared feature representation; initializing, using the first set of parameters and the second set of parameters, a neural network classifier; and training, using training data from the aligned first set of image data and the second set of image data, the neural network classifier to generate a similarity metric for the first and second imaging modalities. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A system comprising:
-
a first scanner configured to capture a first set of image data of a volume with a first imaging modality; a second scanner configured to capture a second set of image data of the volume with a second imaging modality; and a processor configured to; receive, from the first scanner and the second scanner over a network, the first set of image data and the second set of image data; rigidly align the first set of image data and the second set of image data; train a first set of parameters with a multimodal stacked denoising auto encoder to generate a shared feature representation of the first set of image data and the second set of image data; train a second set of parameters with a denoising auto encoder to generate a transformation of the shared feature representation; initialize, using the first set of parameters and the second set of parameters, a deep neural network classifier; and train, using training data from the aligned first set of image data and the second set of image data, the deep neural network classifier to generate a similarity metric for the first and second imaging modalities. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
-
-
18. A method comprising:
-
receiving, from a first scanner, a first set of image data captured of a volume using a first imaging modality; receiving, from a second scanner, a second set of image data captured of the volume using a second imaging modality; identifying, by the processor using a trained similarity metric for multimodal image data, which voxels from the first set of image data that correspond to the same position in the volume as voxels from the second set of image data; and performing image fusion on the first set of image data and the second set of image data using the identified voxels. - View Dependent Claims (19, 20)
-
Specification