Abstract: The present disclosure relates to techniques for segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis.

Abstract: The present disclosure relates to techniques for segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis.

Abstract: In one embodiment, a method includes accessing a first scan image from a set of computed tomography (CT) scan images with each CT scan image being at a first resolution, generating a first downscaled image of the first scan image by resampling the first scan image to a second resolution that is lower than the first resolution, determining coarse segmentations corresponding to organs portrayed in the first scan image by first machine-learning models based on the first downscaled image, extracting segments of the first scan image based on the coarse segmentations with each extracted segment being at the first resolution, determining fine segmentations corresponding to the respective organs portrayed in the extracted segments by second machine-learning models based on the extracted segments, and generating a segmented image of the first scan image based on the fine segmentations, wherein the segmented image comprises confirmed segmentations corresponding to the organs.

Abstract: Embodiments disclosed herein generally relate to connected machine learning models with joint training for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) image, wherein the three-dimensional MRI image depicts a region of a brain of a subject, wherein the region of the brain includes at least a first type of lesions and a second type of lesions; inputting the three-dimensional MRI image into a machine-learning model comprising a first convolutional neural network and a second convolutional neural network; generating a first segmentation mask for the first type of lesions using the first convolutional neural network that takes as input the three-dimensional MRI image; generating a second segmentation mask for the second type of lesions using the second convolutional neural network that takes as input the three-dimensional MRI image; and outputting the first segmentation mask and the second segmentation mask.

Abstract: Embodiments disclosed herein generally relate to multi-arm machine learning models for lesion detection. Particularly, aspects of the present disclosure are directed to accessing a three-dimensional magnetic resonance imaging (MRI) images. Each of the three-dimensional MRI images depict a same volume of a brain of a subject. The volume of the brain includes at least part of one or more lesions. Each three-dimensional MRI image of the three-dimensional MRI images is processed using one or more corresponding encoder arms of a machine-learning model to generate an encoding of the three-dimensional MRI image. The encodings of the three-dimensional MRI images are concatenated to generate a concatenated representation. The concatenated representation is processed using a decoder arm of the machine-learning model to generate a prediction that identifies one or more portions of the volume of the brain predicted to depict at least part of a lesion.

Abstract: Systems and methods relate to processing optical tomography coherence (OCT) images to predict characteristics of a treatment to be administered to effectively treat age-related macular degeneration. The processing can include pre-processing the image by flattening and/or cropping the image and processing the pre-processed image using a neural network. The neural network can include a deep convolutional neural network. An output of the neural network can indicate a predicted frequency and/or interval at which a treatment (e.g., anti-vascular endothelial growth factor therapy) is to be administered so as to prevent leakage of vasculature in the eye.

Abstract: Medical image(s) are input into a detection network to generate mask(s) identifying a set of regions within the medical image(s), where the detection network predicts that each region identified in the mask(s) includes a depiction of a tumor of one or more tumors within the subject. For each region, the region of the medical image(s) is processed using a tumor segmentation network to generate one or more tumor segmentation boundaries for the tumor present within the subject. For each tumor and by using a plurality of organ-specific segmentation networks, an organ is determined within which at least part of the tumor is located. An output is generated based on the one or more tumor segmentation boundaries and locations of the organs within which at least part of the one or more tumors are located.

Abstract: The present disclosure relates to techniques for segmenting tumors with positron emission tomography (PET) using deep convolutional neural networks for image and lesion metabolism analysis.