96 results about "Chest radiograph" patented technology

A method, system, and computer program product for modifying an appearance of an anatomical structure in a medical image, e.g., rib suppression in a chest radiograph. The method includes: acquiring, using a first imaging modality, a first medical image that includes the anatomical structure; applying the first medical image to a trained image processing device to obtain a second medical image, corresponding to the first medical image, in which the appearance of the anatomical structure is modified; and outputting the second medical image. Further, the image processing device is trained using plural teacher images obtained from a second imaging modality that is different from the first imaging modality. In one embodiment, the method also includes processing the first medical image to obtain plural processed images, wherein each of the plural processed images has a corresponding image resolution; applying the plural processed images to respective multi-training artificial neural networks (MTANNs) to obtain plural output images, wherein each MTANN is trained to detect the anatomical structure at one of the corresponding image resolutions; and combining the plural output images to obtain a second medical image in which the appearance of the anatomical structure is enhanced.

A method for automatically segmenting lung regions in a chest radiographic image comprising; providing an input digital chest radiograph image; preprocessing the input digital radiographic image; extracting the chest body midline and lung centerlines from the preprocessed image. Locating one-by-one, the chest body model, the spine model and the two lung models in the image based on the extracted chest body midline and two lung centerlines; and detecting the lung contours by deforming the lung shape models to converge to the true lung boundaries as a function of the extracting and locating image processing.

The invention discloses an X-ray chest radiograph image quality determination method and device. The method comprises: conducting normalization processing on an X-ray chest radiograph image; inputtingthe normalized image into a deep learning model for image separation and recognition to obtain at least one separation result; wherein the deep learning model at least comprises one of the followingformulas: a pulmonary lobe segmentation model, a spine segmentation model, a shoulder blade segmentation model and a foreign matter detection model; and determining the image quality of the X-ray chest radiograph based on the separation result. The X-ray chest radiograph image quality is fully automatically evaluated, and the image quality determination speed is high. Moreover, a technician can behelped to control the image quality of the chest radiograph, and the radiograph reading accuracy is indirectly improved.

Acquisition techniques for dual energy (DE) chest imaging system. Technique factors include the added x-ray filtration, kVp pair, and the allocation of dose between low- and high-energy projections, with total dose equal to or less than that of a conventional chest radiograph. Factors are described which maximize lung nodule detectability as characterized by the signal difference to noise ratio (SDNR) in DE chest images. kVp pair and dose allocation are described using a chest phantom presenting simulated lung nodules and ribs for thin, average, and thick body habitus. Low- and high-energy techniques ranged from 60-90 kVp and 120-150 kVp, respectively, with peak soft-tissue SDNR achieved at [60 / 120] kVp for patient thicknesses and levels of imaging dose. A strong dependence on the kVp of the low-energy projection was observed.

A method for identifying a projection view and an orientation of a chest radiograph. The method includes the steps of: providing an input digital image of the chest radiograph; preprocessing the input digital radiographic image; classifying the view of the input digital radiographic image; and determining the orientation of the input digital radiographic image.

The invention relates generally to the field of cancer detection, diagnosis, subtyping, staging, prognosis, treatment and prevention. More particularly, the present invention relates to methods for the detection, and / or diagnosing and / or subtyping and / or staging of lung cancer in a patient. Based on a particular panel of biomarkers, the present invention provides methods to detect, diagnose at an early stage and / or differentiate small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) and within NSCLC to differentiate between squamous cell carcinomas (SCC), adenocarcinomas (AC), within SCC to discriminate G2 and G3 stage and within lung cancer to differentiate for lung cancers with or without neuroendocrine origin. It further provides the use of said panel of biomarkers in monitoring disease progression in a patient, including both in vitro and in vivo imaging techniques. The in vitro imaging techniques typically include an immunoassay detecting protein or antibody of the biomarkers on a sample taken from said patient, e.g. serum or tissue sample. The in vivo imaging techniques typically include chest radiographs (X-rays), Computed Tomography (CT) imaging, spiral CT, Positron Emission Tomography (PET), PET-CT and scintigraphy for molecular imaging and diagnosis and to monitor disease progression and treatment response in patients. It is accordingly a further aspect to provide a kit to perform the aforementioned diagnosing and / or subtyping and / or staging assay and the imaging techniques, comprising reagents to determine the gene expression or protein level of the aforementioned panel of biomarkers for in vitro and in vivo applications.

The invention relates generally to the field of cancer detection, diagnosis, subtyping, staging, prognosis, treatment and prevention. More particularly, the present invention relates to methods for the detection, and / or diagnosing and / or subtyping and / or staging of lung cancer in a patient. Based on a particular panel of biomarkers, the present invention provides methods to detect, diagnose at an early stage and / or differentiate small cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC) and within NSCLC to differentiate between squamous cell carcinomas (SCC), adenocarcinomas (AC), within SCC to discriminate G2 and G3 stage and within lung cancer to differentiate for lung cancers with or without neuroendocrine origin. It further provides the use of said panel of biomarkers in monitoring disease progression in a patient, including both in vitro and in vivo imaging techniques. The in vitro imaging techniques typically include an immunoassay detecting protein or antibody of the biomarkers on a sample taken from said patient, e.g. serum or tissue sample. The in vivo imaging techniques typically include chest radiographs (X-rays), Computed Tomography (CT) imaging, spiral CT, Positron Emission Tomography (PET), PET-CT and scintigraphy for molecular imaging and diagnosis and to monitor disease progression and treatment response in patients. It is accordingly a further aspect to provide a kit to perform the aforementioned diagnosing and / or subtyping and / or staging assay and the imaging techniques, comprising reagents to determine the gene expression or protein level of the aforementioned panel of biomarkers for in vitro and in vivo applications.

The invention relates to an X-ray chest radiography classification device based on an improved dense connection network. The device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, and the processor is used for realizing the following method steps when executing the program: step 1, chest radiography preprocessing; 2, constructing and training a deep convolutional neural network; the method comprises the following steps of: modifying a classification network DenseNet121, adding an extrusion-excitation module into the network ina dense connection mode to form an improved dense connection network, constructing a loss function conforming to the task, and training the network by using a preprocessed chest radiography; and step3, testing the network and selecting an optimal network model: testing the network model obtained by each round of training on a test set, and selecting the network model with the highest average AUCvalue as a final model.

The embodiment of the invention provides a pulmonary tuberculosis intelligent recognition method and system with image symptom interpretation. The pulmonary tuberculosis intelligent recognition methodcomprises the steps: preprocessing an X ray chest radiograph so as to be converted into a vector diagram; carrying out abnormal region identification to obtain a classification result of whether a suspected focus exists or not; judging whether a suspected focus is identified in the abnormal area or not; performing classification correction processing to obtain a conditional probability of the suspected lesion; judging whether a suspected focus caused by pulmonary tuberculosis exists in the abnormal area or not; processing a suspected area corresponding to the suspected focus on an original image to obtain a sub-image vector diagram; explaining that the abnormal region has image characterization significance to obtain characterization description to judge whether the abnormal region has pulmonary tuberculosis or not; and based on the eigenimage description, carrying out pulmonary tuberculosis activity discrimination. According to the embodiment of the invention, the internal relation between the image symptom features in the chest radiography can be effectively obtained, and the judgment logic of iconography can be better met compared with the judgment made only based on the image,and the recognition precision and efficiency can be greatly improved.

A method of generating a pulmonary nodule image from a chest radiograph. The method includes the steps of: producing a map of a clear lung field; removing low frequency variation from the clear lung field to generate a level image; and performing at least one grayscale morphological operation on the level image to generate a nodule-bone image. Pulmonary nodules can be detected using the nodule-bone image by the further steps of: pulmonary nodules from a chest radiograph. The method includes the steps of: identifying candidate nodule locations in the nodule-bone image; segmenting a region around each candidate nodule location in the nodule-bone image; and using the features of the segmented region to determine if a candidate is a nodule.