Abstract: The present disclosure relates to a method, an apparatus, and a system for detecting and classifying an ischemic stroke based on a medical image. A medical image based ischemic stroke detecting and type classifying apparatus according to an aspect of the present disclosure includes an acquiring unit which collects images related to a brain of at least one patient; a detecting unit which determines whether the at least one patient is a large vessel occlusion patient, based on the collected image; a determining unit which determines whether a type of the large vessel occlusion is embolism or intracranial atherosclerosis (ICAS), when the at least one patient is a large vessel occlusion patient; and a diagnosing unit which provides treatment direction information which is applied differently according to the determined type of the large vessel occlusion.

Abstract: A device and a method for detecting a tumor using a medical image and diagnosing a shape and a property of the detected tumor are disclosed. An exemplary medical image-based tumor detection and diagnostic device includes: an input unit configured to obtain a medical image related to a patient; a preprocessing unit configured to preprocess the obtained medical image to observe a tumor region; an analysis unit configured to divide the preprocessed image into a plurality of regions by applying a deep neural network-based deep learning technique; and a measurement unit configured to group the plurality of divided regions by performing clustering on the plurality of divided regions. The measurement unit extracts a group feature value in respect to each of the grouped regions and derives diagnosis information related to the tumor based on the extracted group feature value.

Abstract: A device and a method for detecting a tumor using a medical image and diagnosing a shape and a property of the detected tumor are disclosed. An exemplary medical image-based tumor detection and diagnostic device includes: an input unit configured to obtain a medical image related to a patient; a preprocessing unit configured to preprocess the obtained medical image to observe a tumor region; an analysis unit configured to divide the preprocessed image into a plurality of regions by applying a deep neural network-based deep learning technique; and a measurement unit configured to group the plurality of divided regions by performing clustering on the plurality of divided regions. The measurement unit extracts a group feature value in respect to each of the grouped regions and derives diagnosis information related to the tumor based on the extracted group feature value.

Abstract: The present disclosure relates to a method, an apparatus, and a system for detecting and classifying an ischemic stroke based on a medical image. A medical image based ischemic stroke detecting and type classifying apparatus according to an aspect of the present disclosure includes an acquiring unit which collects images related to a brain of at least one patient; a detecting unit which determines whether the at least one patient is a large vessel occlusion patient, based on the collected image; a determining unit which determines whether a type of the large vessel occlusion is embolism or intracranial atherosclerosis (ICAS), when the at least one patient is a large vessel occlusion patient; and a diagnosing unit which provides treatment direction information which is applied differently according to the determined type of the large vessel occlusion.

Abstract: A biological classification device and a method for Alzheimer's disease using a brain image are disclosed. The biological classification device includes an image receiving unit which receives a plurality of images obtained by capturing images of a brain of a subject; an image processing unit which acquires neurodegeneration feature related to the brain of the subject and standardized uptake value ratio (SUVR) information from the plurality of images; an image analysis unit which performs first determination of a presence or absence of cranial nerve abnormality based on the neurodegeneration feature(s) and second determination and third determination of a presence or absence of abnormality of beta amyloid protein and tau protein, respectively, based on the SUVR information; and a classifying unit which performs biological classification of the subject related to Alzheimer's disease using the first, the second, and the third determinations together.

Abstract: Disclosed is a sinus balloon catheter. The sinus balloon catheter includes a body unit, a bending angle adjusting unit that includes a bending angle adjusting lever and a rotary plate, and a guide unit that includes a balloon, and has a pair of bending angle adjusting wires therein. The guide unit includes a first guide body, and a second guide body that is slidably provided in the first guide body, the balloon is provided on the second guide body, the rotary plate is connected to any one of the pair of bending angle adjusting wires to be rotated, and the other bending angle adjusting wire is maintained within the guide unit, and the guide unit is inserted into any one of a sphenoidal sinus, a frontal sinus, and a maxillary sinus.

Abstract: Disclosed is a sinus balloon catheter. The sinus balloon catheter includes a body unit, a bending angle adjusting unit that includes a bending angle adjusting lever and a rotary plate, and a guide unit that includes a balloon, and has a pair of bending angle adjusting wires therein. The guide unit includes a first guide body, and a second guide body that is slidably provided in the first guide body, the balloon is provided on the second guide body, the rotary plate is connected to any one of the pair of bending angle adjusting wires to be rotated, and the other bending angle adjusting wire is maintained within the guide unit, and the guide unit is inserted into any one of a sphenoidal sinus, a frontal sinus, and a maxillary sinus.