A method for a system and method for morphometric detection of malignancy associated change (MAC) is disclosed including the acts of obtaining a sample; imaging cells to produce 3D cell images for each cell; measuring a plurality of different structural biosignatures for each cell from its 3D cell image to produce feature data; analyzing the feature data by first using cancer case status as ground truth to supervise development of a classifier to test the degree to which the features discriminate between cells from normal or cancer patients; using the analyzed feature data to develop classifiers including, a first classifier to discriminate normal squamous cells from normal and cancer patients, a second classifier to discriminate normal macrophages from normal and cancer patients, and a third classifier to discriminate normal bronchial columnar cells from normal and cancer patients.

A signal translating method may include, according to one aspect of the present application, receiving a source signal of a first domain; identifying erroneous features and effective features from the source signal; translating the source signal of the first domain into a first virtual signal of a second domain, the first virtual signal is that in which erroneous features included in the source signal has been removed; and outputting the first virtual signal. Therefore, the virtual signal of the second domain in which the erroneous features removed may be output.

The present disclosure discloses a method for image rendering. The method may include obtaining first image data by a first processing device; performing a first rendering operation on the first image data; and determining second image data based on the first image data. The method may further include receiving a non-rendering operation on the second image data by the second processing device; and performing a second rendering operation on the non-rendered second image data.

An information acquisition method includes: executing a voxel defining process to divide an area in which a signal source is assumed to be present and define a voxel division V1 specifying resolution of an image; executing a data collecting process to acquire magnetic field data resulting from measurement of a magnetic field generated in the area; and executing a reconstructing process to estimate, by using a mathematical algorithm, a direction and strength of a current of a signal source at a location of each voxel based on the acquired magnetic field data. The reconstructing process includes: calculating a Gram matrix by using a voxel division V2 defined coarser than the voxel division V1; and reconstructing, by using the Gram matrix, a direction and strength of a current of a signal source in the voxel division V1.

The present disclosure provides an image processing method and apparatus, an electronic device, and a computer-readable storage medium. The method includes: obtaining a first three-dimensional image of a target object in a three-dimensional coordinate system; determining a target plane of the target object in the first three-dimensional image, the target plane comprising target three-dimensional points; projecting the target three-dimensional points to a two-dimensional coordinate system defined on the target plane, to obtain target two-dimensional points; determining a target polygon and a minimum circumscribed target graphic of the target polygon according to the target two-dimensional points; and recognizing the minimum circumscribed target graphic as a first target graphic of the target object in the first target three-dimensional image.

Image reconstruction is an inverse problem that solves for a computational image based on sampled sensor measurement. Sparsely sampled image reconstruction poses addition challenges due to limited measurements. In this work, we propose an implicit Neural Representation learning methodology with Prior embedding (NeRP) to reconstruct a computational image from sparsely sampled measurements. The method differs fundamentally from previous deep learning-based image reconstruction approaches in that NeRP exploits the internal information in an image prior, and the physics of the sparsely sampled measurements to produce a representation of the unknown subject. No large-scale data is required to train the NeRP except for a prior image and sparsely sampled measurements. In addition, we demonstrate that NeRP is a general methodology that generalizes to different imaging modalities such as CT and MRI. We also show that NeRP can robustly capture the subtle yet significant image changes required for assessing tumor progression.

The present disclosure relates to a C-shaped arm for use with a medical imaging system. In accordance with certain embodiments, the C-shaped arm comprises a C-shaped portion, a radiation source carried by the C-shaped portion, and a radiation detector carried by the C-shaped portion, wherein at least a portion of the C-shaped portion is formed of a unidirectional ultra-high modulus carbon fiber material.

An X-ray imaging system including: an X-ray Talbot imaging apparatus which is provided with an object table, an X-ray source, a plurality of gratings, and an X-ray detector side by side in a direction of an X-ray radiation axis, and irradiates the X-ray detector with an X-ray from the X-ray source through an object and the plurality of gratings to obtain a moire image required for forming a reconstruction image of the object; and an object housing inside which the object is housed and an environmental condition independent of an external environment is set, wherein the object housing is provided detachably with respect to the object table.

A method is for actuating a medical imaging device for generating a second three-dimensional image dataset including a target region in a region of interest of a patient with a functional impairment. The method includes providing a first three-dimensional image dataset including the region of interest of the patient; identifying the target region based on the first three-dimensional image dataset, a partial region of the region of interest with the functional impairment being determined; determining an imaging parameter for generating the second three-dimensional image dataset based on the identified target region; and actuating the medical imaging device based on the imaging parameter for the generation of the second three-dimensional image dataset.

The present disclosure relates to a medical microscope field. A stereo microscope connected to an optical coherence tomography (OCT) unit for forming a tomographic image of a target object includes an objective lens unit including a plurality of lenses each having an aperture of a predetermined size, a pair of first magnification lens units each including a plurality of lenses having a pair of magnification lens apertures positioned within the aperture of the objective lens unit, a second magnification lens unit including a plurality of lenses having an OCT aperture disposed separately from the pair of magnification lens aperture within the aperture of the objective lens unit, and a light delivery unit configured to receive light from the OCT unit and deliver the light to the second magnification lens unit and configured to deliver light received from the second magnification lens unit to the OCT unit.