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.

Embodiments access a radiological image of tissue having a tumoral volume and a peritumoral volume; define a vasculature associated with the tumoral volume; generate a Cartesian two-dimensional (2D) vessel network representation; compute a first set of localized Hough transforms based on the Cartesian 2D vessel network representation; generate a first aggregated set of peak orientations based on the first set of Hough transforms; generate a spherical 2D vessel network representation; compute a second set of localized Hough transforms based on the spherical 2D vessel network representation; generate a second aggregated set of peak orientations based on the second set of Hough transforms; generate a vascular network organization descriptor based on the aggregated peak orientations; compute a probability that the tissue is a member of a positive class based on the vascular network organization descriptor; classify the ROI based on the probability; and display the classification.

An information processing method includes deducing a diagnosis name derived from a medical image on the basis of an image feature amount corresponding to a value indicating a feature of a medical image, deducing an image finding representing a feature of the medical image on the basis of the image feature amount, and presenting the image finding deduced in the deducing the image finding which is affected by an image feature amount common to the image feature amount that has affected the deduction of the diagnosis name in the deducing the diagnosis name and the diagnosis name to a user.

Computed tomography (CT) screening, diagnosis, or another image analysis tasks are performed using one or more networks and/or algorithms to either integrate complementary tomographic image reconstructions and radiomics or map tomographic raw data directly to diagnostic findings in the machine learning framework. One or more reconstruction networks are trained to reconstruct tomographic images from a training set of CT projection data. One or more radiomics networks are trained to extract features from the tomographic images and associated training diagnostic data. The networks/algorithms are integrated into an end-to-end network and trained. A set of tomographic data, e.g., CT projection data, and other relevant information from an individual is input to the end-to-end network, and a potential diagnosis for the individual based on the features extracted by the end-to-end network is produced. The systems and methods can be applied to CT projection data, MRI data, nuclear imaging data, ultrasound signals, optical data, other types of tomographic data, or combinations thereof.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

In one embodiment, a diagnosis support apparatus includes: an input circuit configured to acquire a first medical image; and processing circuitry configured to generate a second medical image from the first medical image in such a manner that information included in the second medical image is reduced from information included in the first medical image, extract auxiliary information from the first medical image, and perform inference of a disease by using the second medical image and the auxiliary information.

For anomaly detection based on topogram predication from surface data, a sensor captures the outside surface of a patient. A generative adversarial network (GAN) generates a topogram representing an interior anatomy based on the outside surface of the patient. An X-ray image of the patient is acquired and compared to the generated topogram. By quantifying the difference between the real X-ray image and the predicted one, anatomical anomalies may be detected.

The medical image processing apparatus includes a medical image acquisition unit that acquires a medical image; and a lesion detection unit that detects a lesion region in the medical image. The lesion detection unit includes a first identifier that identifies a lesion region candidate in the medical image and a second identifier that identifies whether the lesion region candidate identified by the first identifier is a blood vessel region, and detects the lesion region candidate that is not identified as the blood vessel region by the second identifier as the lesion region.

An object recognition method is performed at an electronic device. The method includes: pre-processing a target image, to obtain a pre-processed image, the pre-processed image including three-dimensional image information of a target region of a to-be-detected object, processing the pre-processed image by using a target data model, to obtain a target probability, the target probability being used for representing a probability that an abnormality appears in a target object in the target region of the to-be-detected object; and determining a recognition result of the target region of the to-be-detected object according to the target probability, the recognition result being used for indicating the probability that the abnormality appears in the target region of the to-be-detected object. The object recognition method can effectively improve accuracy of object recognition and avoid a case of incorrect recognition.

Disclosed are a server and a server-based medical image analysis method. A medical image analysis server according to an embodiment of the present invention includes at least one processor. The at least one processor is configured to: automatically transmit a retrieval query to a first database in which medical images are stored; control a receiving interface so that the receiving interface receives a first medical image satisfying the retrieval query from the first database; perform image processing on the first medical image and extract at least one first region of interest from the first medical image; quantify a first feature extracted for the first medical image and the at least one first region of interest; and store the first feature in a second database in association with the first medical image and the retrieve condition.