A method and a device for registering three-dimensional data are disclosed. The method for registering three-dimensional data comprises: generating first two-dimensional data by two-dimensionally converting first three-dimensional data indicating a surface of a three-dimensional model of a target, generating second two-dimensional data by two-dimensionally converting second three-dimensional data indicating at least a part of the three-dimensional surface of the target; determining a first matching region in the first two-dimensional data and a second matching region in the second two-dimensional data by matching the second two-dimensional data to the first two-dimensional data; setting, as initial position, a plurality of points of the first three-dimensional data, which correspond to the first matching region and a plurality of points of the second three-dimensional data, which correspond to the second matching region; and registering the first three-dimensional data and the second three-dimensional data using the initial position.

Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Highly accurate shape registration processing is performed. In the image processing apparatus, data, which correspond to N frames (N is an integer not less than 2), in units of frames including a plurality of three-dimensional models per frame is acquired. Then, shape registration processing is performed for the acquired three-dimensional models corresponding to the N frames by using information indicating a correspondence relationship of the three-dimensional models between frames of the N frames.

A method for printing on an item, that includes generating a scanned shape of the item, performing a lookup of the scanned shape in a shape database, making a determination that the scanned shape matches a saved shape in the shape database, and based on the determination, measuring an item rotation of the item, generating a transformed print image based on the item rotation, causing a printhead to print on the item using the transformed print image.

An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

A medical system includes an instrument, a display system, and a processing unit. The instrument includes an instrument shape sensor. The processing unit includes one or more processors. The processing unit is configured to, receive an anatomic model of a patient anatomy, receive shape sensor data from the instrument shape sensor while the instrument is positioned within the patient anatomy and registered to the anatomic model, determine a preferred fluoroscopic image plane for display on the display system based on the received shape sensor data and the area of interest, and provide an indication on the display system to guide positioning of a fluoroscopy system to obtain a fluoroscopic image in the preferred fluoroscopic image plane. An area of interest is identified in the anatomic model.

A method comprises identifying a treatment planning image of a target subject, the treatment planning image comprising information associated with an arrangement of structures within the target subject. The method further comprises generating, based on the information, a set of reference data associated with the target subject, the reference data indicating a plurality of positions of the target subject. The method further comprises generating target-subject-specific models based on the reference data and modifying one or more hyper-parameters of the target-subject-specific mode to generate second target-subject-specific models corresponding to a second position of the plurality of positions. The method further comprises controlling a radiation treatment delivery device based on the second target-subject-specific model to deliver a radiation treatment to the target subject.

In certain embodiments, the systems, apparatus, and methods disclosed herein relate to robotic surgical systems with built-in navigation capability for patient position tracking and surgical instrument guidance during a surgical procedure, without the need for a separate navigation system. Robotic based navigation of surgical instruments during surgical procedures allows for easy registration and operative volume identification and tracking. The systems, apparatus, and methods herein allow re-registration, model updates, and operative volumes to be performed intra-operatively with minimal disruption to the surgical workflow. In certain embodiments, navigational assistance can be provided to a surgeon by displaying a surgical instrument’s position relative to a patient’s anatomy. Additionally, by revising pre-operatively defined data such as operative volumes, patient-robot orientation relationships, and anatomical models of the patient, a higher degree of precision and lower risk of complications and serious medical error can be achieved.

A method for predicting the morphological changes of liver tumor after ablation based on deep learning includes: obtaining a medical image of liver tumor before ablation and a medical image of liver tumor after ablation; preprocessing the medical image of liver tumor before ablation and the medical image of liver tumor after ablation; obtaining a preoperative liver region map, postoperative liver region map, and postoperative liver tumor residual image map; obtaining a transformation matrix by a Coherent Point Drift (CPD) algorithm and obtaining a registration result map according to the transformation matrix; training the network by a random gradient descent method to obtain a liver tumor prediction model; using the liver tumor prediction model to predict the morphological changes of liver tumor after ablation. The method provides the basis for quantitatively evaluating whether the ablation area completely covers the tumor and facilitates the postoperative treatment plan for the patient.

Various embodiments comprise systems and methods to model the shape of a target subject to coregister an image generated by an on-subject sensor array to the anatomy of the subject. In some examples, a system constrains sensors to follow the contour of the target subject. The system generates a surface contour representation of the target subject based on the locations of the individual ones of the sensors. The system fits the surface contour representation of the target subject to an outer surface feature of an anatomical scan.