Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Abstract: Camera-based respiratory triggered medical scanning is provided. A camara captures an image or images of a patient. A processor detects breathing from the camera image or images. Detected breathing is used to automatically trigger the medical scan. An extra breathing sensor is not needed. Manual monitoring is not relied upon.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

Abstract: Patient biographic data may be estimated by receiving patient image data, applying the patient image data to a machine learned model, the machine learned model trained on second patient data and trained to map the second patient data to associated biographic data using machine learned features, generating the patient biographic data based on the applying and the machine learned features, and outputting the patient biographic data. The patient biographic data may include a patient weight, a patient height, a patient gender, and a patient age.

Abstract: For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.

Abstract: For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: A method of obtaining a medical image includes obtaining, via a camera, at least one surface image of a patient. A pose of the patient is determined from the at least one surface image of the patient using at least one spatial information module. The patient is positioned, via a moveable bed, to an imaging start position and a medical image of the patient is obtained using a medical imaging modality.