A localized evaluation network incorporates a discriminator acting as classifier, which may be included within a generative adversarial network (GAN). GAN may include a generative network such as U-NET for creating segmentations. The localized evaluation network is trained on image pairs including medical images of organs of interest and segmentation (mask) images. The network is trained to distinguish whether an image pair does or does not represent the ground truth. GAN examines interior layers of the discriminator and evaluates how much each localized image region contributes to the final classification. The discriminator may analyze regions of the image pair that contribute to a classification by analyzing layer weights of the machine learning model. Disclosed embodiments include a visual attribute, such as a heat map, that represents contributions of localized regions of a contour to an overall confidence score. These localized regions may be highlighted and reported for quality assurance review.

Embodiments of the present application provide a medical image synthesis device and method. According to an embodiment, a method includes acquiring a first medical image and a second medical image and registering the first medical image with the second medical image. The method includes determining a first parameter value at each pixel location on the registered first medical image and a second parameter value at each pixel location on the second medical image. The method includes multiplying the first parameter value with the second parameter value at the same pixel location on the registered first medical image and the second medical image and generating synthetic image data based on the multiplication result.

Disclosed herein is a medical instrument (100, 300). Execution of the machine executable instructions causes a processor (106) to: receive (206) a set of joint location coordinates (128) for a subject (118) reposing on a subject support (120), receive (207) a body orientation (132) in response to inputting the set of joint location coordinates into a predetermined logic module (130), calculate (208) a torso aspect ratio (134) from set of joint location coordinates. If (210) the torso aspect ratio is greater than a predetermined threshold (136) then (212) the body pose of the subject is a decubitus pose. Execution of the machine executable instructions further cause the processor to assign (220) the body pose as being a supine pose if the subject is face up on the subject support or assign (222) the body pose as being a prone pose if the subject is face down on the subject support if the torso aspect ratio is less than or equal to the predetermined threshold. Execution of the machine executable instructions further cause the processor to generate (216) a subject pose label (142).

The present disclosure provides methods and apparatus for evaluating tissue structure in damaged or healing tissue. The present disclosure also provides methods of identifying a patient at the onset of risk of pressure ulcer or at risk of the onset of pressure ulcer, and treating the patient with anatomy-specific clinical interventions selected, based on thermal imaging (TI). The present disclosure also provides methods of stratifying groups of patients based on risk of wound development and methods of reducing incidence of tissue damage in a care facility. The present disclosure also provides methods to analyze trends of TI intensities to detect tissue damage before it is visible, and methods to compare bisymmetric TI intensities to identify damaged tissue.

A framework for multi-scan image processing. A single real anatomic image of a region of interest is first acquired. One or more emission images of the region of interest are also acquired. One or more synthetic anatomic images may be generated based on the one or more emission images. One or more deformable registrations of the real anatomic image to the one or more synthetic anatomic images are performed to generate one or more registered anatomic images. Attenuation correction may then be performed on the one or more emission images using the one or more registered anatomic images to generate one or more attenuation corrected emission images.

The present disclosure relates to a method of determining a prognostic outlook for patients having metastatic breast cancer. The method includes receiving imaging data from an image of a patient that is receiving or that is to receive cycline dependent kinase 4 and 6 (CDK 4/6) inhibitor therapy for hormone receptor-positive (HR+) metastatic breast cancer. Radiomic heterogeneity features are extracted from imaging data associated with a metastasis within the imaging. A prognostic marker is determined from the radiomic heterogeneity features. The prognostic marker is indicative of a response of the patient to CDK 4/6 inhibitor therapy for HR+ metastatic breast cancer.

The present disclosure relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including generating an imaging data set having both scan data and digitized biopsy data from a patient with small cell lung cancer (SCLC). Scan derived features are extracted from the scan data and biopsy derived features are extracted from the digitized biopsy data. A radiomic-pathomic risk score (RPRS) is calculated from one or more of the scan derived features and one or more of the biopsy derived features. The RPRS is indicative of a prognosis of the patient.

A learning apparatus includes a hardware processor. The hardware processor obtains a first medical image. The hardware processor generates a second medical image in which a feature according to feature information related to a portion other than a recognition target of the first medical image is made close to the feature according to standard feature information which is to be a predetermined standard. The second medical image is used to train a machine learning model.

A method for determining an ROI in medical imaging may include receiving first position information related to a body contour of a subject with respect to a support from a flexible device configured with a plurality of position sensors. The flexible device may be configured to conform to the body contour of the subject, and the support may be configured to support the subject. The method may also include generating a 3D model of the subject based on the first position information. The method may further include determining an ROI of the subject based on the 3D model of the subject.

The present disclosure provides an operation method of a PET (positron emission tomography) quantitative localization system, which includes steps as follows. The PET image and the MRI (magnetic resonance imaging) of the patient are acquired; the nonlinear deformation is performed on the MRI and the T1 template to generate deformation information parameters; the AAL (automated anatomical labeling) atlas is deformed to an individual brain space of the patient, so as to generate an individual brain space AAL atlas, where the AAL atlas and the T1 template are in a same space; lateralization indexes of the ROIs of the individual brain space AAL atlas corresponding to the PET image normalized through the gray-scale intensity are calculated; the lateralization indexes are inputted into one or more machine learning models to analyze the result of determining a target.