A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

A method of reading a medical image by a computing device operated by at least one processor is provided. The method includes obtaining an abnormality score of the input image using an abnormality prediction model, filtering the input image so as not to be subsequently analyzed when the abnormality score is less than or equal to a cut-off score based on the cut-off score which makes a specific reading sensitivity; and obtaining an analysis result of the input image using a classification model that distinguishes the input image into classification classes when the abnormality score is greater than the cut-off score.

A method for analysing images in a computer aided diagnosis system (CADx) to provide a first image analysis score and a second image analysis score for an image is described. The method comprising; receiving an input comprising at least one input image showing all or part of the lungs of a subject; analysing the input to calculating a first image analysis value and a second image analysis value for the input and processing the calculated values to generate corresponding first image analysis and second image analysis scores and outputting at least one of the first image analysis score and the second image analysis score for the subject. A computer aided diagnosis system (CADx) and a method of training a computer aided diagnosis system are also described.

A machine learning device includes: a generation unit generating a first shape model representing a shape of an object before deformation and a second shape model representing a shape of the object after the deformation based on measurement data before and after the deformation; and a learning unit learning a feature amount including a difference value between each micro region and another micro region that constitute the first shape model, and a relation providing a displacement from the each micro region of the first shape model to each corresponding micro region of the second shape model.

A processor derives a first feature amount for each of a plurality of first objects included in first data by a first neural network, derives a second feature amount for second data including one or more second objects by a second neural network, specifies a first object candidate, which is paired with the second object, from among the plurality of first objects, estimates an attribute of a pair second object, which is paired with the first object candidate, based on the first object candidate, and trains at least one of the first neural network or the second neural network such that a difference between the estimated attribute of the pair second object and an attribute of the pair second object derived from the second data is reduced.

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.

The invention relates to a system for assessing a pulmonary image which allows for an improved assessment with respect to lung nodules detectability. The pulmonary image is smoothed for providing different pulmonary images (20, 21, 22) with different degrees of smoothing, wherein signal values and noise values, which are indicative of the lung vessel detectability and the noise in these images, are determined and used for determining an image quality being indicative of the usability of the pulmonary image to be assessed for detecting lung nodules. Since a pulmonary image shows lung vessels with many different vessel sizes and with many different image values, which cover the respective ranges of potential lung nodules generally very well, the image quality determination based on the different pulmonary images with different degrees of smoothing allows for a reliable assessment of the pulmonary image's usability for detecting lung nodules. The image quality is used to determine a radiation dose level to be applied for generating a next pulmonary image.

A system and method are presented for applying convolutional neural networks (CNNs) to aid in rapid on-site evaluation cytopathology. Image data are acquired from a biopsy slide. Areas of interest are determined using a first CNN. The image data is segmented into image tiles, and tiles showing the areas of interest are analyzed using a second CNN to assign a histologic category to the slide. The second CNN also utilizes site specific data relating to the biopsy location. Layered image data from multiple focal planes can be acquired of the slide and used as input to the second CNN. Categorized tiles are sorted and presented to a remote computing system for cytopathology determinations, aided by the results of applying the second CNN. Semantic segmentation can also be developed, both as input to the second CNN and as data presented to the remote computer system.

Embodiments include controlling a processor to access a radiological image of a region of lung tissue, where the radiological image includes a ground glass (GGO) nodule; define a tumoral region by segmenting the GGO nodule, where defining the tumoral region includes defining a tumoral boundary; define a peri-tumoral region based on the tumoral boundary; extract a set of radiomic features from the peri-tumoral region and the tumoral region; provide the set of radiomic features to a machine learning classifier trained to distinguish minimally invasive adenocarcinoma (MIA) and adenocarcinoma in situ (AIS) from invasive adenocarcinoma; receive, from the machine learning classifier, a probability that the GGO nodule is invasive adenocarcinoma, where the machine learning classifier computes the probability based on the set of radiomic features; generate a classification of the GGO nodule as MIA or AIS, or invasive adenocarcinoma, based, at least in part, on the probability; and display the classification.

A method for lung nodule evaluation is provided. The method may include obtaining a target image including at least a portion of a lung of a subject. The method may also include segmenting, from the target image, at least one target region each of which corresponds to a lung nodule of the subject. The method may further include generating an evaluation result with respect to the at least one lung nodule based on the at least one target region.