Systems and methods are provided for classifying a tissue mass as malignant or benign. The method includes identifying a region of interest in the tissue mass in computed tomography (CT) imaging data, segmenting the CT imaging data to delimit at least a portion of the tissue mass into image segments, extracting a set of radiomic parameters indicative of the tissue mass. The set of radiomic parameters may include tissue mass location, tissue mass shape, tissue mass surface characteristic, or tissue mass texture distribution indicative of the amount of image segments identified. The method also includes generating a report classifying the indeterminate lung nodule as being malignant or benign based on the set of radiomic parameters of the indeterminate lung nodule

A method and system of automated segmentation of an anatomical object through learned examples include: receiving, by a processing device, an image of the anatomical object; determining a sparse representation of a shape of the anatomical object by iteratively evolving a segmenting surface as a combination of a level set segmentation and a linear combination of training shapes; and outputting, to an output device, the sparse representation of the shape of the anatomical object as the segmentation of the anatomical object.

The present invention proposes an ultrasound system and a method of detecting lung sliding on the basis of a temporal sequence of ultrasound data frames of a first region of interest. The first region of interest includes a pleural interface of a lung. A sub-region identifier (410) is configured to identify, for each of the ultrasound data frames, a sub-region of a scanned region of the ultrasound data frame, the sub-region comprising at least part of the pleural interface; a lung sliding detector (420) is configured to derive a parametric map for the sub-region on the basis of at least two ultrasound data frames of the temporal sequence, parametric values of the parametric map indicating a degree of tissue motion over the at least two ultrasound frames; wherein the lung sliding detector is further configured to extract data of the sub-regions from the at least two ultrasound data frames, and to derive the parametric map on the basis of the extracted data.

In one embodiment, a medical image processing apparatus includes memory circuitry configured to store a program; and processing circuitry configured, by executing the program, to set a region of interest in a medical image, set an analysis region in the region of interest by reducing the region of interest, and calculate feature amount in the analysis region.

A mechanical ventilation assessment assistance device includes at least one electronic processor; and a non-transitory storage medium storing instructions readable and executable by the at least one electronic processor to perform a mechanical ventilation assessment assistance method including obtaining an image of a patient (P) receiving mechanical ventilation; generating or updating a patient-specific lung model of at least one lung of the patient based on the obtained image; simulating a response of the patient to a mechanical ventilation therapy using the generated or updated patient-specific lung model; comparing the simulated response with an actual response of the patient to the mechanical ventilation therapy; based on the comparison, determining an imaging recommendation for acquiring an image of at least one lung of the patient; and outputting the determined imaging recommendation.

An information processing apparatus includes an acquisition unit, a specification unit, a determination unit, a generation unit, and a display control unit. The acquisition unit acquires a three-dimensional image containing at least a tubular structure. The specification unit specifies a first point inside the tubular structure and specifies a lesion outside the tubular structure in the three-dimensional image. The determination unit determines whether a blood vessel is present in a region between the first point and the lesion based on signal values of voxels of the three-dimensional image in the region between the first point and the lesion. The generation unit generates a two-dimensional image of the tubular structure viewed from the first point based on the three-dimensional image. The display control unit displays information indicating a region of the lesion on the two-dimensional image to be distinguishable in determining whether a blood vessel is present in the region.

A method is provided to perform local de-noising of an image. The method includes obtaining a region of interest and a region of noise within a scan. The method also includes determining, for a first image based on the region of interest and a second image based on the region of noise, sample blocks and atoms for each image, where each atom contributes to a weighted sum that approximates a sample block in the image. The method also includes determining a measure of similarity of each atom from the first image with atoms from the second image and removing an atom from the first image if the measure of similarity exceeds a predetermined threshold value. The method also includes reconstructing a de-noised image based on atoms remaining in the first image after removing the atom from the first image, and presenting the de-noised image on a display device.

A content-based medical image retrieval method and a retrieval system using the same include: obtaining m (2mn) number of unit images from a three-dimensional (3D) medical image including n (n2) number of unit images and extracting features per unit image from each of the m (2mn) number of unit images through a feature extraction unit, wherein the 3D medical image is voxel data including a plurality of slices and each of the plurality of slices is defined as a unit image; inputting features of each unit image extracted from the m (2mn) number of unit images to a recurrent neural network to generate an output value; and performing medical image retrieval using the output value through an input processing unit, wherein a plurality of 3D medical images to be compared with the output value include a 3D medical image having p (p2, pn) number of unit images.

The present disclosure is directed to a method and system for automatically detecting a physiological condition from a medical image of a patient. The method may include receiving the medical image acquired by an imaging device. The method may further include detecting, by a processor, target objects and obtaining the corresponding target object patches from the received medical image. And the method may further include determining, by the processor, a first parameter using a first learning network for each target object patch. The first parameter represents the physiological condition level of the corresponding target object, and the first learning network is trained by adding one or more auxiliary classification layers. This method can quickly, accurately, and automatically predict target object level and/or image (patient) level physiological condition from a medical image of a patient by means of a learning network, such as 3D learning network.

A computer-implemented method for automatically detecting a target object from a 3D image is disclosed. The method may include receiving the 3D image acquired by an imaging device. The method may further include detecting, by a processor, a plurality of bounding boxes as containing the target object using a 3D learning network. The learning network may be trained to generate a plurality of feature maps of varying scales based on the 3D image. The method may also include determining, by the processor, a set of parameters identifying each detected bounding box using the 3D learning network, and locating, by the processor, the target object based on the set of parameters.