A computed tomography (CT) pulmonary nodule detection method based on deep learning is provided. The method comprises the steps of: acquiring 3D pulmonary CT sequence images of a user; processing the acquired 3D pulmonary CT sequence images into 2D image data; inputting 2D image data into a preset deep learning network model for training to obtain a trained pulmonary nodule detection model; inputting a set of 3D pulmonary CT sequence images to be tested into the trained pulmonary nodule detection model to obtain a preliminary pulmonary nodule detection result; applying a pulmonary region segmentation algorithm based on deep learning to the preliminary pulmonary nodule detection result to remove false positive pulmonary nodules, so as to obtain a final pulmonary nodule detection result.

Systems and methods for generating synthesized images are provided. An input medical image patch, a segmentation mask, a vector of appearance related parameters, and manipulable properties are received. A synthesized medical image patch including a synthesized nodule is generated based on the input medical image patch, the segmentation mask, the vector of appearance related parameters, and the manipulable properties using a trained object synthesis network. The synthesized nodule is synthesized according to the manipulable properties. The synthesized medical image patch is output.

Systems and methods for providing a novel framework to simulate the appearance of pathology on patients who otherwise lack that pathology. The systems and methods include a simulator that is a generative adversarial network (GAN). Rather than generating images from scratch, the systems and methods discussed herein simulate the addition of diseases-like appearance on existing scans of healthy patients. Focusing on simulating added abnormalities, as opposed to simulating an entire image, significantly reduces the difficulty of training GANs and produces results that more closely resemble actual, unmodified images. In at least some implementations, multiple GANs are used to simulate pathological tissues on scans of healthy patients to artificially increase the amount of available scans with abnormalities to address the issue of data imbalance with rare pathologies.

A feature information acquisition unit acquires the first feature information on the first medical image and second feature information on the second medical image having an imaging time different from an imaging time of the first medical image. A sentence creation unit compares the first feature information and the second feature information generated by the feature information acquisition unit, and creates a sentence representing a change between the first medical image and the second medical image. A display control unit causes the display unit to display a sentence representing a change.

Disclosed are a method and a device for detecting pulmonary nodule in Computed Tomography (CT) image, as well as a computer-readable storage medium. The method for detecting pulmonary nodule in CT image includes: obtaining a CT image to be detected, performing a pixel segmentation processing on the CT image through a pre-stored three-dimensional convolutional neural pixel segmentation network, to obtain a probability graph corresponding to the CT image, and obtaining a candidate nodule region by marking a connected domain on the probability graph; and predicting the candidate nodule region by various pre-stored prediction models corresponding to different three-dimensional convolutional neural network classifiers, to obtain various probability prediction values of the candidate nodule region, and comprehensively processing the various probability prediction values to obtain a classification result of the candidate nodule region.

An X-ray imaging system using multiple pulsed X-ray sources to perform highly efficient and ultrafast 3D radiography is presented. There are multiple pulsed X-ray sources mounted on a structure in motion to form an array of sources. The multiple X-ray sources move simultaneously relative to an object on a pre-defined arc track at a constant speed as a group. Electron beam inside each individual X-ray tube is deflected by magnetic or electrical field to move focal spot a small distance. When focal spot of an X-ray tube beam has a speed that is equal to group speed but with opposite moving direction, the X-ray source and X-ray flat panel detector are activated through an external exposure control unit so that source tube stay momentarily standstill equivalently. 3D scan can cover much wider sweep angle in much shorter time and image analysis can also be done in real-time.

Disclosed is a system and a method for determining a brock score. A CT scan image may be resampled into a plurality of slices using a bilinear interpolation. A nodule may be detected on one or more of the plurality of slices. A region of interest associated with the nodule may be identified using an image processing technique. Further, a nodule segmentation may be performed to remove an area surrounding the region of interest. Subsequently, a plurality of characteristics associated with the nodule may be identified automatically using a deep learning model. Finally, a brock score for the patient may be determined based on the plurality of characteristics and demographic data of the patient.

An information processing apparatus includes an image feature acquiring unit configured to acquire first image features and second image features from a medical image and a deriving unit configured to derive image findings of a plurality of items belonging to a first finding type based on the first image features and deriving image findings of a plurality of items belonging to a second finding type different from the first finding type based on the second image features that at least partly differ from the first image features.

Disclosed herein are methods for determining a subject level risk of metastatic cancer involving the training and/or deployment of models to determine 1) a lymph node level risk of individual lymph node involvement and/or 2) a subject level risk of lymph node involvement. Thus, the methods can identify patients who are high or low risk for having nodal disease and optionally enable the guided intervention of cancer patients, for example, via treatment.

Systems and methods are provided for generating a synthesized medical image patch of a nodule. An initial medical image patch and a class label associated with a nodule to be synthesized are received. The initial medical image patch has a masked portion and an unmasked portion. A synthesized medical image patch is generated using a trained generative adversarial network. The synthesized medical image patch includes the unmasked portion of the initial medical image patch and a synthesized nodule replacing the masked portion of the initial medical image patch. The synthesized nodule is synthesized according to the class label. The synthesized medical image patch is output.