Embodiments of the present invention provide a method for identifying a body position of a detection object in medical imaging, a medical imaging system, and a computer-readable storage medium. The method comprises: receiving an image group by a trained deep learning network, the image group comprising a plurality of pre-scan images in a plurality of directions obtained by pre-scanning a detection object; and outputting body position information of the detection object by the deep learning network.

Method, executed by a computer, for identifying a coronary sinus of a patient, comprising: receiving a 3D image of a body region of the patient; extracting 2D axial images of the 3D image taken along respective axial planes, 2D sagittal images of the 3D image taken along respective sagittal planes, and 2D coronal images of the 3D image taken along respective coronal planes; applying an axial neural network to each 2D axial image to generate a respective 2D axial probability map, a sagittal neural network to each 2D sagittal image to generate a respective 2D sagittal probability map, and a coronal neural network to each 2D coronal image to generate a respective 2D coronal probability map; generating, based on the 2D probability maps, a 3D mask of the coronary sinus of the patient.

Systems, methods, and executable programs for providing lung candidacy information to health care professionals. A method includes receiving three-dimensional image data categorized as lung lobe voxels, airway voxels, or lung fissure voxels. A fissure integrity score is generated for the lung fissure voxels. First perspective transparent views of the categorized lung lobe voxels, the categorized airway voxels, and the categorized lung fissure voxels are generated based on a first point of view. The first perspective view of the lung fissure voxels includes a visual representation of fissure integrity based on the generated fissure integrity scores for the corresponding voxels. A report is generated that includes the generated views. The report is outputted.

A reconstruction unit generates a plurality of tomographic images representing a plurality of tomographic planes of a subject by reconstructing a plurality of projection images acquired by performing tomosynthesis imaging. A synthesis unit synthesizes the plurality of tomographic images to generate a composite two-dimensional image. A display control unit displays the composite two-dimensional image on a display, and in a case where one tissue of a first tissue and a second tissue that are present in the subject in association with each other is selected in the displayed composite two-dimensional image, emphasizes and displays the selected one tissue and the other tissue associated with the selected one tissue.

Disclosed is a system and a method for monitoring a CT scan image. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A region of interest may be identified on each slice using an image processing technique. The region of interest may be masked on each slice using deep learning. Subsequently, a nodule may be detected as the region of interest using the deep learning. Further, a plurality of characteristics associated with the nodule may be identified. Furthermore, an emphysema may be detected in the region of interest on each slice. A malignancy risk score for the patient may be computed. A progress of the nodule may be monitored across subsequent CT scan images. Finally, a report of the patient may be generated.

A system for processing breast tissue images includes an image processing computer and a user interface operatively coupled to the image processing computer, wherein the image processing computer is configured to obtain image data of breast tissue, processing the image data to generate a set of reconstructed image slices, the reconstructed image slices collectively depicting the breast tissue, process respective subsets of the reconstructed image slices to generate a set of image slabs, each image slab comprising a synthesized 2D image of a portion of the breast tissue obtained from a respective subset of the set of reconstructed image slices.

Methods and systems for navigating to a target through a patient's bronchial tree are disclosed including a bronchoscope, a probe insertable into a working channel of the bronchoscope including a location sensor, and a workstation in operative communication with the probe and the bronchoscope the workstation including a user interface that guides a user through a navigation plan and is configured to present a three-dimensional (3D) view for displaying a 3D rendering of the patient's airways and a corresponding navigation plan, a local view for assisting the user in navigating the probe through peripheral airways of the patient's bronchial tree to the target, and a target alignment view for assisting the user in aligning a distal tip of the probe with the target.

A processor is configured to generate a structure-highlighted synthesized two-dimensional image from a plurality of tomographic images, to detect a structure of interest from the plurality of tomographic images or the structure-highlighted synthesized two-dimensional image, and to set at least some of the plurality of tomographic images as either storage-required images or non-storage-required images based on a detection result of the structure of interest.

A learning device detects a first region of interest including a calcification and a second region of interest including an other lesion on the basis of any one of a composite two-dimensional image obtained from a plurality of projection images captured by tomosynthesis imaging or a plurality of tomographic images, the tomographic image, or a normal two-dimensional image, and trains an image generation model, in which a weight for the first region of interest is largest and a weight for the second region of interest is set to be equal to or larger than a weight for a region other than the first region of interest and the second region of interest, by updating a weight for a network of the image generation model on the basis of a loss between a pseudo two-dimensional image output by the image generation model and the normal two-dimensional image and/or the composite two-dimensional image to reduce the loss.

Various systems are provided for non-uniform thickness and/or sampling of slabs of the breast to present DBT acquisitions. A method for generating a patient image as a set of slabs representing an imaged object, the method comprising acquiring a tomosynthesis projection, reconstructing a series of slab images, each slab representing a portion of a breast, and a plurality of slabs of non-uniform thickness and/or non-uniform sampling in a 3D reconstructed domain defined by x-, y-, and z-axes.