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 method of providing in vivo navigation of a medical device includes: receiving input medical imaging data of a patient's anatomy; receiving input non-optical in vivo image data from a sensor on a distal end of the device in the anatomy; using a trained model to locate the distal end in the input imaging data, wherein: the model is trained, based on (i) training medical imaging data and training non-optical in vivo image data of one or more individuals' anatomy and (ii) registration data associating the training image data with locations in the training imaging data as ground truth, to learn associations between the training image data and the training imaging data; determining an output location of the medical device using the learned associations and the input data; modifying the input imaging data to depict the determined location; and causing a display to output the modified input imaging data.

A pneumonia area detecting system includes an information obtaining module, an image preprocessing module, a calculating module, and a pneumonia area detecting model. The information obtaining module obtains medical images and a frame size of an initial pneumonia area. The image preprocessing module processes the medical images to obtain a standard format image. The calculating module processes the frame size of the initial pneumonia area through a preset algorithm and the standard format image, to obtain a frame size of a standard format pneumonia area. The pneumonia area detecting model obtains an output pneumonia area according to the standard format image and the frame size of the standard format pneumonia area. A pneumonia area detecting method and an electronic device are also provided in the present disclosure.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.

Various embodiments of the present disclosure are directed towards a method for predicting a response to treatment of small cell lung cancer (SCLC). The method includes generating a radiomic risk score (RRS) for the patient based on a plurality of radiomic features, wherein the RRS is prognostic of overall survival (OS) of the patient. The RRS is provided to a machine learning classifier that is trained to predict a response of the patient to a SCLC chemotherapy treatment based, at least in part, on the RRS. The machine learning classifier provides a classification of the patient into either a responder group (RG) or a non-responder group (NRG), where the NRG indicates the patient will not respond to the SCLC chemotherapy treatment and the RG indicates that the patient will respond to the SCLC chemotherapy treatment.

A diagnostic support program that is possible to display a movement of an organ is provided.

A diagnostic support program that analyzes images of an organ of a human and displays analysis results, the program causing a computer to execute a process comprising: processing of acquiring a plurality of frame images, processing of calculating a cyclic change that characterizes a state of an organ between each of the frame images, processing of Fourier-transforming the cyclic change that characterizes the state of the organ, processing of extracting a spectrum in a fixed band including a spectrum corresponding to a frequency of a movement of an organ out of a spectrum obtained after the Fourier-transforming, processing of performing inverse Fourier transform on the spectrum extracted from the fixed band, and processing of outputting each of the images after performing the inverse Fourier transform, is provided.

The current disclosure provides methods and systems for rapidly and consistently determining medical image quality metrics following acquisition of a diagnostic medical image. In one embodiment, the current disclosure teaches a method for determining an image quality metric by, acquiring a medical image of an anatomical region, mapping the medical image to a positional attribute of an anatomical feature using a trained deep neural network, determining an image quality metric based on the positional attribute of the anatomical feature, determining if the image quality metric satisfies an image quality criterion, and displaying the medical image, the image quality metric, and a status of the image quality criterion via a display device. In this way, a diagnostic scanning procedure may be expedited by providing technicians with real-time insight into quantitative image quality metrics.

A method for evaluation of medical image data comprises: providing medical image data of a patient to be examined; determining, for at least one segment of the medical image data, a respective classification probability value with respect to at least one classification from a list of specified classifications; determining a patient-specific relevance criterion for at least one classification for at least the at least one segment of the medical image data; and determining a clinical relevance of the at least one classification for the at least one segment of the medical image data using the patient-specific relevance criterion, and at least one of based on the classification probability values or based on the at least one segment of the medical image data.

A system may comprise a processor and a memory having computer readable instructions stored thereon. The computer readable instructions, when executed by the processor, may cause the system to record shape data from a shape sensor for an instrument during an image capture period and generate a sensor point cloud from the recorded shape data. The computer readable instructions, when executed by the processor, may also cause the system to receive image data from an imaging system during the image capture period, generate an image point cloud for the instrument from the image data, and register the sensor point cloud to the image point cloud.

Embodiments of the disclosure provide methods and systems for representation learning from a biomedical image with a sparse convolution. The exemplary system may include a communication interface configured to receive the biomedical image acquired by an image acquisition device. The system may further include at least one processor, configured to extract a structure of interest from the biomedical image. The at least one processor is also configured to generate sparse data representing the structure of interest and input features corresponding to the sparse data. The at least one processor is further configured to apply a sparse-convolution-based model to the biomedical image, the sparse data, and the input features to generate a biomedical processing result for the biomedical image. The sparse-convolution-based model performs one or more neural network operations including the sparse convolution on the sparse data and the input features.