A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.

A system for determining and visualizing damage to an anatomical joint of a patient. The system is to: obtain a three dimensional image representation of an anatomical joint which is based on a medical image stack; determine damage to an anatomical structure in the anatomical joint by analyzing the medical image stack; mark damage to the anatomical structures in the obtained three dimensional image representation; obtain a 3D model based on the three dimensional image representation; and create a graphical user interface (GUI). The GUI may comprise: functionality to visualize and enable manipulation of the at least one 3D model; functionality to enable removal of the visualization of the anatomical structure from the 3D model; functionality to visualize and enable browsing of the medical image stack; and functionality to visualize the position of the medical image that is currently visualized.

Systems and methods for assessing a disease are provided. An input medical image in a first modality is received. Lungs are segmented from the input medical image using a trained lung segmentation network and abnormality patterns associated with the disease are segmented from the input medical image using a trained abnormality pattern segmentation network. The trained lung segmentation network and the trained abnormality pattern segmentation network are trained based on 1) synthesized images in the first modality generated from training images in a second modality and 2) target segmentation masks for the synthesized images generated from training segmentation masks for the training images. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality patterns.

The present disclosure relates to determining a biological tissue structural marker for diagnosis of a disease using a biological tissue characterization technique. A method for determining a structural marker for a diagnosis of a disease can include measuring a first molecular structure of a biological tissue of a first population of animals, after a carcinogenic or pathogenic substance was introduced into the first population. A second molecular structure of the biological tissue of a second population of animals can be measured, wherein the second population did not receive the carcinogenic or pathogenic substance. A first and a second structural marker of the molecular structures of the biological tissue of the first and second populations, respectively, can be identified and compared to determine that the first structural marker is indicative that the biological tissue of the first population of animals was affected by the disease.

An arrival position information estimation unit that estimates arrival position information at which an extension line of a peripheral branch included in a bronchial region arrives at a surface of a lung region, and estimates assumed mapping information other than the arrival position information of a specific peripheral branch on the basis of a preset condition in a case where the specific peripheral branch is set as a target branch, and a display control unit that generates a mapping image obtained by mapping the arrival position information of the specific peripheral branch and the assumed mapping information to a surface of the lung region and displays the mapping image on a display unit are included.

Provided is a radiation detector that can allow an operator to more accurately identify a position of a body tissue into which radionuclides have been taken. A radiation detector includes: a probe which has a radiation detection element housed therein and which is insertable into a body; a reporting part provided to the probe; and a control part configured to cause the reporting part to operate in accordance with a result of detection of radiation, the detection being made by the radiation detection element.

A medical scan report labeling system is operable to transmit a medical report that includes natural language text to a first client device for display. Identified medical condition term data is received from the first client device in response. An alias mapping pair in a medical label alias database is identified by determining that a medical condition term of the alias mapping pair compares favorably to the identified medical condition term data. A medical code that corresponds to the alias mapping pair and a medical scan that corresponds to the medical report are transmitted to a second client device of an expert user for display, and accuracy data is received from the second client device in response. The medical code is mapped to the first medical scan in the medical scan database when the accuracy data indicates that the medical code compares favorably to the medical scan.

A medical report natural language model includes an artificial neural network implemented via the processor and is trained based a plurality of medical reports wherein each of the medical reports is mapped to at least one medical code of a plurality of medical codes and further based a plurality of medical condition terms from a plurality of alias mapping pairs, wherein each of the plurality of medical condition terms are unique, wherein each of the plurality of medical condition terms indicates a corresponding medical condition and wherein each of the plurality of alias mapping pairs includes a one of the plurality of medical condition terms and a corresponding single one of the plurality of medical codes that is a deterministic function of the one of the plurality of medical condition terms and wherein the plurality of alias mapping pairs includes two or more alias mapping pairs that map a corresponding two or more of the plurality of medical condition terms to a same one of the plurality of medical codes. A system operates by determining a first medical code of the plurality of medical codes utilizing the medical report natural language model on the first medical report by: recognizing, via the artificial neural network of the medical report natural language model, a first medical condition term of the plurality of medical condition terms in the first medical report; and determining the first medical code of the plurality of medical codes, based on the plurality of alias mapping pairs, wherein the determining the first medical code includes identifying a first alias mapping pair of the plurality of alias mapping pairs that pairs the first medical condition term deterministically to the first medical code; and mapping the first medical code of the plurality of medical codes and the first medical condition term of the plurality of medical condition terms to a first medical scan corresponding to the first medical report.

An imaging conditions acquisition unit acquires imaging conditions relevant to the positioning of an irradiation device and a radiation detector for acquiring a medical image. An imaging control unit controls the irradiation device and the like so as to acquire a pre-shot image or the medical image of the subject. A correction amount calculation unit calculates the correction amount of the imaging conditions based on the pre-shot image. A notification unit sends notification of the correction amount.

Disclosed herein is an apparatus suitable for radiation detection. The apparatus may comprise a radiation absorption layer and a first electrode on the radiation absorption layer. The radiation absorption layer may be configured to generate charge carriers therein from a radiation particle absorbed by the radiation absorption layer. The first electrode may be configured to generate an electric field in the radiation absorption layer. The first electrode may have a geometry shaping the electric field so that the electric field in an amplification region of the radiation absorption layer has a field strength sufficient to cause an avalanche of the charge carriers in the amplification region.