Abstract:
An apparatus and method are provided including a first segmenter and a second segmenter. The first segmenter is configured to generate a first segmentation result from a medical image using a first segmentation parameter for a candidate lesion. The second segmenter is configured to determine a target lesion to segment from among the candidate lesion based on the first segmentation result, and generate a second segmentation result using a second segmentation parameter to segment the target lesion.
Abstract:
An apparatus and a method for combining three-dimensional ultrasound images are provide. The method involves obtaining a plurality of three-dimensional ultrasound image data that corresponds to a Region of Interest (ROI); detecting one or more landmarks, using a parameter for detection; outputting the detection result and receiving a response from a user; registering each of one or more selected landmarks as link information according to a received response from the user; and generating a combined three-dimensional ultrasound image by combining at least two pieces of three-dimensional ultrasound image data using at least one of the one or more selected landmarks registered as the link information, wherein the at least two pieces of three-dimensional ultrasound image data commonly comprise the at least one of the one or more selected landmarks.
Abstract:
Provided are apparatuses and methods for analyzing a lesion in an image. A Threshold Adjacency Statistics (TAS) feature may be extracted from a medical image, and a pattern of the lesion may be classified using the extracted TAS feature.
Abstract:
Disclosed is a user interface which enables mark based interaction for images. The present disclosure relates to a user interface which enables mark based interaction for images, the images comprising a volume which is a three-dimensional image and slices which are two-dimensional images, each of which represents a cross section of the volume. At least two of the images each include the same visual mark for identifying at least one common region of interest. The user interface comprises: an input unit for receiving a user input associated with the same visual mark included in one of the images; and at least one component for enabling the interaction for the images including the same visual mark associated with the user input.
Abstract:
An apparatus and method for detecting a lesion, which enables to adaptively determine a parameter value of a lesion detection process using a feature value extracted from a received medical image and a parameter prediction model to improve accuracy in lesion detection and lesion diagnosis. The apparatus and the method include a model generator configured to generate a parameter prediction model based on pre-collected medical images, an extractor configured to extract a feature value from a received medical image, and a determiner configured to determine a parameter value of a lesion detection process using the extracted feature value and the parameter prediction model.
Abstract:
An apparatus and method for medical diagnostics includes receiving three-dimensional (3D) volume data of a part of a patient's body, and generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction. The apparatus and the method also determine whether a lesion in each of the 2D slices is benign or malignant and output results indicative thereof, select a number of the 2D slices based on the results, and make a final determination whether the lesion is benign or malignant based on the selected 2D slices.
Abstract:
An apparatus and method for medical diagnostics includes receiving three-dimensional (3D) volume data of a part of a patient's body, and generating two-dimensional (2D) slices including cross-sections of the 3D volume data cut from a cross-section cutting direction. The apparatus and the method also determine whether a lesion in each of the 2D slices is benign or malignant and output results indicative thereof, select a number of the 2D slices based on the results, and make a final determination whether the lesion is benign or malignant based on the selected 2D slices.