Abstract: An example for processing patient image data workflows can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Abstract: Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

Abstract: The present discussion relates to patient image data workflows. One example can temporarily serially arrange a set of semantic labeling modules in a patient image data workflow pipeline responsive to receiving an event trigger. The example can also remove the set of modules from the patient image data workflow pipeline responsive to receiving an event completion trigger.

Abstract: The description relates to secure patient information handling. One example can receive encrypted patient data from a first entity. The example can receive a request to view the encrypted patient data from a second entity. The request can include a conditional access code and the example can validate the conditional access code. In an instance where the conditional access code is valid, the example can retrieve an encryption key for the encrypted patient data. The example can decrypt at least a portion of the encrypted patient data to produce decrypted patient data. The example can provide at least some of the decrypted patient data to the second entity.

Abstract: Automatic organ localization is described. In an example, an organ in a medical image is localized using one or more trained regression trees. Each image element of the medical image is applied to the trained regression trees to compute probability distributions that relate to a distance from each image element to the organ. At least a subset of the probability distributions are selected and aggregated to calculate a localization estimate for the organ. In another example, the regression trees are trained using training images having a predefined organ location. At each node of the tree, test parameters are generated that determine which subsequent node each training image element is passed to. This is repeated until each image element reaches a leaf node of the tree. A probability distribution is generated and stored at each leaf node, based on the distance from the leaf node's image elements to the organ.

Abstract: The present discussion relates to automated image data processing and visualization. One example can facilitate generating a graphical user-interface (GUI) from image data that includes multiple semantically-labeled user-selectable anatomical structures. This example can receive a user selection of an individual semantically-labeled user-selectable anatomical structure. The example can locate a sub-set of the image data associated with the individual semantically-labeled user-selectable anatomical structure and can cause presentation of the sub-set of the image data on a subsequent GUI.

Abstract: Transrectal ultrasound is used for accurate planning and targeting of placement of radioactive seeds by needles into the prostate in transperineal prostate brachytherapy. The pubic arch formed by the union of the pelvic bones is a potential barrier to passage of the needles containing the radioactive seeds. An initial image of the pubic arch is also provided by transrectal ultrasound, in addition to a transverse cross-sectional image of the prostate. The initial image of the pubic arch is processed using a technique to selectively enhance the contrast of the linear features of the edge of the ultrasound image. The enhanced image is then thresholded via the use of a percentile thresholding technique and then a curve modeling the shape of the pubic arch is fitted against the thresholded, enhanced image to produce a processed image of the pubic arch. This is then overlaid with the image of the prostate and a determination made as to pubic arch interference.