VUNO Inc.

The present disclosure relates to a method of aiding visualization of lesions in a medical image and an apparatus using the same. In detail, according to the method of the present disclosure, a lesion visualization aiding method may receive a user input for selecting a single point in the medical image to modify a lesion mask representing a lesion area in the medical image, may determine a modified lesion mask corresponding to the received user input among a plurality of pre-generated plurality of candidate lesion masks, and may provide the modified lesion mask with the medical image.

An image providing method performed by a computing apparatus includes acquiring a first image group including at least a portion of a series of images generated for continuous volumes with a first slice thickness belonging to a subject, providing, as a current viewing image, one image of the first image group or one image of a second image group including images generated for continuous volumes with a second slice thickness belonging to the subject, and in response to a first specific input of an input device, repeatedly updating an image provided as the current viewing image with an individual image provided for a subsequent viewing based on a directivity given for the first specific input and, in response to a second specific input of the input device, switching the current viewing image between an image of the first image group and an image of the second image group.

Disclosed is a method for detecting a serial section of a medical image, which is performed by a computing device. The method may include: detecting segments included in at least one tissue which exists in the medical image; estimating a number of tissue sections corresponding to the serial section and a distance between the segments based on the segments; and distinguishing tissue sections corresponding to the serial section based on the estimated number of tissue sections corresponding to a serial section and the distance between the segments.

According to the embodiment of the present disclosure, a method of analyzing a lesion based on a medical image performed by a computing device is disclosed. The method may include: extracting, by using a pre-trained artificial neural network, a first feature for each tile of a plurality of tiles included in an input image; and extracting, by using the pre-trained artificial neural network, a second feature for an entirety of the input image, based on information about whether the lesion is present for the each tile and information on a pattern of the lesion for the each tile generated based on first features of the plurality of tiles.

Disclosed is a method for analyzing a lesion based on a medical image, which is performed by a computing device. The method may include: obtaining positional information of a suspicious nodule which exists in the medical image; generating a mask for the suspicious nodule based on a patch of the medical image corresponding to the positional information; and determining a class for a state of the suspicious nodule based on the patch of the medical image and the mask for the suspicious nodule.

Provided are a method for determining whether an examinee is infected by microorganism, and a determination apparatus using the same. Specifically, the determination apparatus according to the present invention obtains an microphotographed image of a biological sample of the examinee; receives the obtained microphotographed image and generates analysis information on the microorganism based on a deep learning model of the examinee; visualize the generated analysis information to provide it, so as to perform at least one of (i) a process of supporting a remote reading on whether the microorganism corresponding to the analysis information exists or not, and (ii) a process of supporting a user of the computing apparatus to read whether the microorganism corresponding to the analysis information exists or not; and provides a final result as its result.

Disclosed is a method for predicting disease based on a medical image performed by a computing device. The method includes: generating a feature vector related to predictive values of brain disease for each of 2D medical images included in a 3D medical image, using a pre-trained first model; estimating importance indicating prediction accuracy for each of the 2D medical images based on the feature vector, using a pre-trained second model; and selecting at least one model input image suitable for prediction of the brain disease from among the 2D medical images based on the importance.

The present disclosure relates to a method for improving reproduction performance of a deep neural network model trained using a group of learning data so that the deep neural network model can exhibit excellent reproduction performance even for target data having a quality pattern different from that of the group, and a device using same. According to the method of the present disclosure, a computing device acquires the target data, retrieves at least one piece of candidate data having a highest similarity to the target data from a learning data representative group including reference data selected from the learning data, performs adaptive pattern transformation on the target data to enable adaptation to the candidate data, and supports transfer of transformed data, which is a result of the adaptive pattern transformation, to the deep neural network model so as to acquire an output value from the deep neural network model.

According to an embodiment of the present disclosure, a method of detecting a white matter lesion based on a medical image performed by a computing device is disclosed. The method may include: receiving a medical image including at least one brain region; and estimating a first white matter lesion and a second white matter lesion based on the medical image using a pre-trained neural network model.

Disclosed is a method for analyzing a thickness of a cortical region, performed by a computing device. The method may include: extracting a plurality of interfaces included in a cortical region based on a mask generated from a medical image including at least one brain region; and estimating a thickness of the cortical region based on the plurality of interfaces.