Fill techniques as implemented by a computing device are described to perform hole filling of a digital image. In one example, deeply learned features of a digital image using machine learning are used by a computing device as a basis to search a digital image repository to locate the guidance digital image. Once located, machine learning techniques are then used to align the guidance digital image with the hole to be filled in the digital image. Once aligned, the guidance digital image is then used to guide generation of fill for the hole in the digital image. Machine learning techniques are used to determine which parts of the guidance digital image are to be blended to fill the hole in the digital image and which parts of the hole are to receive new content that is synthesized by the computing device.

The present application provides a computer-implemented method for generating a digital data set representing a target tooth arrangement, comprising: obtaining a first and a second 3D digital models respectively representing upper jaw teeth and lower jaw teeth under an initial tooth arrangement, where the first and the second 3D digital models are in a predetermined relative positional relationship; extracting a tooth level feature vector from each tooth of the first and second 3D digital models; preliminarily aligning the first and second 3D digital models based on the tooth level feature vectors using a trained first deep neural network; extracting a jaw level feature vector for each tooth of the preliminarily aligned first and second 3D digital models; and further aligning the preliminarily aligned first and second 3D digital models to obtain a target tooth arrangement based on the jaw level feature vectors using a trained second deep neural network.

A method includes assigning, to first voxels in a model of tissue of a chamber of a heart, respective first values of a parameter at respective locations on the tissue, the first voxels representing the locations, respectively. Some of the locations are on an endocardial surface of the tissue, and others of the locations are on an epicardial surface of the tissue. The method further includes assigning respective second values to second voxels in the model, a subset of which represent a portion of the tissue between the endocardial surface and the epicardial surface, by interpolating the first values. Other embodiments are also described.

For three-dimensional segmentation from two-dimensional intracardiac echocardiography imaging, the three-dimension segmentation is output by a machine-learnt multi-task generator. Rather than the brute force approach of training the generator from 2D ICE images to output a 2D segmentation, the generator is trained from 3D information, such as a sparse ICE volume assembled from the 2D ICE images. Where sufficient ground truth data is not available, computed tomography or magnetic resonance data may be used as the ground truth for the sample sparse ICE volumes. The generator is trained to output both the 3D segmentation and a complete volume (i.e., more voxels represented than in the sparse ICE volume). The 3D segmentation may be further used to project to 2D as an input with an ICE image to another network trained to output a 2D segmentation for the ICE image. Display of the 3D segmentation and/or 2D segmentation may guide ablation of tissue in the patient.

A system includes a manipulator and a processing unit. The processing unit is configured to receive, from a position sensor system, a collected set of spatial information for a distal portion of a medical instrument collected at locations within anatomic passageways as a rigid instrument body is moved in an insertion or retraction direction. The processing unit is further configured to receive, from a position measuring device, a set of position information related to a position of the rigid instrument body when the distal portion is at the locations. The processing unit is further configured to, based at least in part on the set of position information, determine a subset of the set of spatial information relative to the environment coordinate space and, based on the subset of spatial information, determine an initial transform for registering the set of spatial information with anatomical model information in a model coordinate space.

A calculation method for causing a computer to execute processing of: acquiring first measurement information including information of a distance to an object measured by a first sensor, and second measurement information including information of a distance to the object measured by a second sensor; acquiring a first vector, a second vector in a different direction from the first vector, and a first translation point from the first measurement information; acquiring information of a third vector treated as a vector parallel to and in a same direction as the first vector, a fourth vector treated as a vector parallel to and in a same direction as the second vector, and a second translation point treated as a same position as the first translation point from the second measurement information; calculating a rotation angle and a translation distance for aligning a point group of the object measured by the second sensor.

This disclosure relates to an intraoperative guidance system for total joint replacement of a joint of a patient by a surgeon. The guidance system comprises: an X-ray imaging device for single-shot application of X-ray radiation to the joint and for etecting X-ray radiation to create a digital image of the joint and an implant component during a total joint replacement surgery; and a computer system configured to: store a digital three-dimensional model of the joint; receive the digital image during the total joint replacement surgery; perform registration between the digital image and the digital three-dimensional model to determine a placement of the implant component in the digital image in relation to the digital three-dimensional model; determine an intraoperative simulated erformance metric and provide an indication to the surgeon of the intraoperative simulated performance metric as an assessment of a current placement of the implant component.

This disclosure relates to an intraoperative localisation system for total joint replacement of a joint of a patient by a surgeon, the joint being associated with a bone. The localisation system comprises: an X-ray imaging device to create a digital X-ray image of the joint and a localisation object during a total joint replacement surgery; a computer system configured to: store a surgical plan comprising a digital three-dimensional model; receive the digital X-ray image of the joint and the localisation object during the total joint replacement surgery; determine a pose of the localisation object relative to the bone or the joint, based on the digital X-ray image; assess the pose of the localisation object against the surgical plan; and provide an indication of a clinical consequence of the pose in relation to the surgical plan to the surgeon.

An inspection apparatus (100) detects an inspection object (90) from first image data (10) in which the inspection object (90) is included. The inspection apparatus (100) generates second image data (20) by performing a geometric transform on the first image data (10) in such a way that a view of the detected inspection object (90) becomes a view satisfying a predetermined reference. In an inference phase, the inspection apparatus (100) detects, by using an identification model for detecting an abnormality of the inspection object (90), an abnormality of the inspection object (90) included in the second image data (20). Further, in a learning phase, the inspection apparatus (100) learns, by using the second image data (20), an identification model for detecting an abnormality of the inspection object (90).

An ultrasound imaging system is for determining stroke volume and/or cardiac output. The imaging system may include a transducer unit for acquiring ultrasound data of a heart of a subject (or an input for receiving the acquired ultrasound data), and a controller. The controller is adapted to implement a two-step procedure, the first step being an initial assessment step, and the second being an imaging step having two possible modes depending upon the outcome of the assessment. In the initial assessment procedure, it is determined whether regurgitant ventricular flow is present. This is performed using Doppler processing techniques applied to an initial ultrasound data set. If regurgitant flow does not exist, stroke volume is determined using segmentation of 3D ultrasound image data to identify and measure the volume of the left or right ventricle at each of end systole and end-diastole, the difference between them giving a measure of stroke volume. If regurgitant flow does exist, stroke volume is determined using Doppler techniques applied to ultrasound data continuously collected throughout a cardiac cycle.