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.

A system, a method, an apparatus, and a medium that are related to a medial image processing or an information processing. An image processing server that performs a plurality of types of image processing receives and a processing request from an information processing apparatus, executes image processing corresponding to the processing request, and returns a processing result to a request source. The information processing apparatus collects an operating log, calculates priority of each of the plurality of types of image processing based on the collected operating log, and updates and manages priority information. The information processing apparatus acquires a new image, determines executable image processing for the acquired image, and transmits, based on the priority of each executable image processing and on a load situation of the image processing server, a processing request to the image processing server in accordance with a criterion of the priority.

Systems and methods of guiding a clinician in a medical procedure for a nodule involve receiving three-dimensional (3D) image data, generating a volumetric vector map based on the 3D image data, and displaying the volumetric vector map in a way, e.g., via a heat map, that assists a clinician in performing a medical procedure. The systems and methods involve identifying volumetric parameters of the nodule in the 3D image data, generating a voxel map based on the volumetric parameters, identifying a maximum attenuation value in the 3D space of the voxel map, applying a differential equation, e.g., a gradient, to the 3D space of the voxel map from a voxel with the maximum attenuation value to other voxels within the voxel map, and generating a volumetric vector map based on the result of applying the differential equation.

Methods, apparatus, and other embodiments predict response to pemetrexed based chemotherapy. One example apparatus includes an image acquisition circuit that acquires a radiological image of a region of tissue demonstrating NSCLC that includes a region of interest (ROI) defining a tumoral volume, a peritumoral volume definition circuit that defines a peritumoral volume based on the boundary of the ROI and a distance, a feature extraction circuit that extracts a set of discriminative tumoral features from the tumoral volume, and a set of discriminative peritumoral features from the peritumoral volume, and a classification circuit that classifies the ROI as a responder or a non-responder using a machine learning classifier based, at least in part, on the set of discriminative tumoral features and the set of discriminative peritumoral features.

Disclosed are a method, a system, and an apparatus for generating guide information, and an electronic device. The method includes: obtaining image data to be identified of a detection target; segmenting the image data to be identified by using a trained image segmentation model to obtain segmented target image data; determining image data of positioning patches arranged on a body surface of the detection target in the target image data as feature points, and establishing a mapping relationship between a preoperative spatial three-dimensional image coordinate system and an intraoperative spatial positioning coordinate system; and generating guide information based on the mapping relationship, to guide a puncture needle to perform puncturing on the detection target. An operation time of a puncture operation can be greatly shortened, and puncture efficiency and accuracy can be improved.

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.

To improve the performance and accuracy of an image segmentation neural network, a cascaded robust learning framework for the segmentation of noisy labeled images includes two stages: a sample selection stage, and a joint optimization stage with label correction. In the first stage, the clean annotated samples are selected for network updating, so that the influence of noisy sample can be interactively eliminated. In the second stage, the label correction module works together with the joint optimization scheme to revise the imperfect labels. Thus, the training of the whole network is supervised by the corrected labels and the original ones.

The present disclosure relates to a method for training a classifier. The method includes: acquiring an original image; determining a candidate target by segmenting the original image based on at least two segmentation models; determining a universal set of features by extracting features from the candidate target; determining a reference subset of features by selecting features from the universal set of features; and determining a classifier by performing classifier training based on the reference subset of features.

An information processing apparatus for making inferences using image data, comprising: a first inference unit configured to perform a first inference using the image data to obtain a first inference result; at least one second inference unit configured to make a second inference different from the first inference, using the image data, to obtain a second inference result; an information-of-interest acquisition unit configured to obtain a first region of interest which is region information focused in the image data, in the obtainment of the first inference result, and to obtain a second region of interest which is region information focused upon in the image data, in the obtainment of the second inference result; and a determination unit configured to determine relatedness of the first region and the second region of interest from an inclusion relation of the first region and the second region of interest in the image data.

A label generating system operates to generate an artificial intelligence model by: training on a training data set that includes the plurality of medical scans with the corresponding global labels; generating testing global probability data by performing an inference function that utilizes the artificial intelligence model on the plurality of medical scans with the corresponding global labels, wherein the testing global probability data indicates a testing set of global probability values corresponding to the set of abnormality classes, and wherein each of the testing set of global probability values indicates a probability that a corresponding one of the set of abnormality classes is present in each of the plurality of medical scans with the corresponding global labels; comparing the testing set of global probability values to a corresponding confidence threshold for each of the plurality of medical scans selected based on the corresponding one of the global labels; generating an updated training data set by correcting ones of the plurality of medical scans having a corresponding one of the testing set of global probability values that compares unfavorably to the corresponding confidence threshold; and retraining the artificial intelligence model based on the updated training set.