Method and system for detection of histopathological plaque features in medical images using deep neural networks

A method of generating guidance information for assessing a risk of a vascular event in a subject through use of a target information-poor image of tissue vasculature of the subject inputted into a trained deep-neural network, comprising:

• receiving the information-poor image of the tissue vasculature of the subject;
• using a neural network that is trained with image pairs comprising mapped information-poor images and information-rich images of a same vasculature, to segment the target information-poor image into a plurality of plaque features captured in the information-poor image by:
• differentiating between plaque and non-plaque pixels of the tissue vasculature, using the trained neural network, to define a plaque area;
• and
• segmenting the target information-poor image to identify plaque features in the target information-poor image wherein the segmenting is adapted to identify a plurality of plaque features in the target information-poor image.

A non-transitory storage medium comprising program code that, when executed by a processor, cause the processor to:

• receive a plurality of image pairs, wherein each image pair comprises an information-poor image of specific vasculature and an information-rich image of the specific vasculature, wherein the information-rich image has been adapted to identify one or more regions showing one or more plaque features;
• and
• for each image pair of the plurality of image pairs:
• perform image registration to map the information-poor image and the information-rich image into a same coordinate system;
• segment the information-poor image as a function of the identified one or more regions of the information-rich image, thereby identifying in the information-poor image the one or more plaque features;
• and
• compare the segmented information-poor image to a ground truth based on the information-rich image to calculate a loss that is back-propagated through the neural network to train the neural network.

A non-transitory storage medium comprising program code that, when executed by a processor, cause the processor to:

• receive the information-poor image of the tissue vasculature of the subject;
• use a neural network that is trained with image pairs comprising mapped information-poor images and information-rich images of a same vasculature, to segment the target information-poor image into a plurality of plaque features captured in the information-poor image by:
• differentiating between plaque and non-plaque pixels of the tissue vasculature, using the trained neural network, to define an area of the plaque;
• and
• segmenting the target information-poor image to identify plaque features in the target information-poor image wherein the segmenting is adapted to identify a plurality of plaque features in the target information-poor image.

A computing device for training a neural network for segmenting an information-poor image to identify a plurality of atherosclerotic plaque features in the information-poor image, comprising:

• a processor;
• memory comprising program code that, when executed by the processor, cause the processor to:
• receive a plurality of image pairs, wherein each image pair comprises an information-poor image of specific vasculature and an information-rich image of the specific vasculature, wherein the information-rich image has been adapted to identify one or more regions showing one or more plaque features;
• for each image pair of the plurality of image pairs:
• perform image registration to map the information-poor image and the information-rich image into a same coordinate system;
• segment the information-poor image as a function of the identified one or more regions of the information-rich image, thereby identifying in the information-poor image the one or more plaque features;
• and
• compare the segmented information-poor image to a ground truth based on the information-rich image to calculate a loss that is back-propagated through the neural network to train the neural network.

A computing device for assessing a risk of a vascular event in a subject through use of an information-poor image of vasculature of the subject inputted into a trained deep-neural network, comprising:

• an input/output interface for receiving the information-poor image;
• a processor;
• and
• memory comprising program code that, when executed by the processor, cause the processor to:
• receive the information-poor image of the tissue vasculature of the subject;
• use a neural network that is trained with image pairs comprising mapped information-poor images and information-rich images of a same vasculature, to segment the target information-poor image into a plurality of plaque features captured in the information-poor image by:
• differentiating between plaque and non-plaque pixels of the tissue vasculature, using the trained neural network, to define an area of the plaque;
• and
• segmenting the target information-poor image to identify plaque features in the target information-poor image wherein the segmenting is adapted to identify a plurality of plaque features in the target information-poor image.

A system for generating guidance information for assessing a risk of a vascular event in a subject through use of a target information-poor image of tissue vasculature of the subject inputted into a trained deep-neural network, the system comprising:

• a client device for receiving the information-poor image of the tissue vasculature of the subject;
• a server operatively connected to the client device for receiving the information-poor image from the client device and comprising a processor and memory comprising program code that, when executed by the processor, cause the processor to:
• use a neural network that is trained with image pairs comprising mapped information-poor images and information-rich images of a same vasculature, to segment the target information-poor image into a plurality of plaque features captured in the information-poor image by:
• differentiating between plaque and non-plaque pixels of the tissue vasculature, using the trained neural network, to define a plaque area;
• segment the target information-poor image to identify plaque features in the target information-poor image wherein the segmenting is adapted to identify a plurality of plaque features in the target information-poor image;
• and
• communicate to said client device data concerning said plaque features in the target information-poor image.