Methods, non-transitory computer readable media, and arthroscopic video segmentation apparatuses and systems that facilitate improved, automatic segmentation analysis of videos of arthroscopic procedures are disclosed. With this technology, a video feed of an arthroscopic surgery can be automatically segmented using machine learning models and one or more tags related to the segments can be associated with the video feed. The generated videos can be output in real time to provide segmented information related to the surgical procedure or can be saved with the one or more segments tagged for playback for training or informational purposes.

A surgical system includes a robotic device, a surgical tool mounted on the robotic device, and a processing circuit. The processing circuit is configured to receive image data of an anatomy, generate a virtual bone model based on the image data, identify a soft tissue attachment point on the virtual bone model, plan placement of an implant based on the soft tissue attachment point, generate a control object based on the placement of the implant, and control the robotic device to confine the surgical tool within the control object.

A non-transitory computer-readable storage medium storing a program causes a computer to perform an analysis process based on a radiation moving image in which a dynamic state of a specific site of a subject is captured. The program includes the analysis process in which, an analysis is performed based on the radiation moving image wherein when a plane in which the specific site is movable is to be a movable plane, the radiation moving image is obtained by irradiating radiation on the specific site in a state in which the radiation is orthogonal to the movable plane.

Methods and systems for image processing are provided. Image data may be obtained. The image data may include a plurality of voxels corresponding to a first plurality of ribs of an object. A first plurality of seed points may be identified for the first plurality of ribs. The first plurality of identified seed points may be labelled to obtain labelled seed points. A connected domain of a target rib of the first plurality of ribs may be determined based on at least one rib segmentation algorithm. A labelled target rib may be obtained by labelling, based on a hit-or-miss operation, the connected domain of the target rib, wherein the hit-or-miss operation may be performed using the labelled seed points to hit the connected domain of the target rib.

A method for automated detection of landmarks from 3D medical image data using deep learning according to the present inventive concept, the method includes receiving a 3D volume medical image, generating a 2D intensity value projection image based on the 3D volume medical image, automatically detecting an initial anatomical landmark using a first convolutional neural network based on the 2D intensity value projection image, generating a 3D volume area of interest based on the initial anatomical landmark and automatically detecting a detailed anatomical landmark using a second convolutional neural network different from the first convolutional neural network based on the 3D volume area of interest.

A processing method for enhancing the image quality of an image, more particularly a digital medical grey scale image, that comprises the steps of a) decomposing an original image into multiple detail images at different resolution levels and/or orientations, b) processing the detail images to obtain processed detail images, c) computing a result image by applying a reconstruction algorithm to the processed detail ages, said reconstruction algorithm being such that if it were applied to the detail images without processing, then said original image or a close approximation thereof would be obtained, the processing of the detail images comprises the steps of: d) calculating at least one conjugate detail image, and e) computing at least one value of the processed detail images as a function of said conjugate detail image and said detail images.

A physical ability evaluation server includes an image processing unit that executes evaluation score calculation processing on a plurality of still images included in a measurement video to calculate a physical ability evaluation score, a physical ability evaluation unit that evaluates the physical ability based on the evaluation score, and an evaluation result notification unit that creates and outputs an evaluation report based on the evaluation result. The evaluation score calculation processing includes a first process of acquiring joint position coordinates by physique estimation for each still image, and a second process of acquiring physique information by segmentation for a first still image corresponding to a first target period, and a third process that calculates the evaluation score of physical ability by a predetermined calculation formula using the information acquired in the first and second processes with respect to a second still image corresponding to a second target period.

Systems, devices, methods, and computer processing products for automatically checking for errors in segmentation (contouring) using heuristic and/or statistical evaluation methods.

Embodiments of the present disclosure provide a software program that displays both a volume as images and segmentation results as surface models in 3D. Multiple 2D slices are extracted from the 3D volume. The 2D slices may be interactively rotated by the user to best follow an oblique structure. The 2D slices can “cut” the surface models from the segmentation so that only half of the models are displayed. The border curves resulting from the cuts are displayed in the 2D slices. The user may click a point on the surface model to designate a landmark point. The corresponding location of the point is highlighted in the 2D slices. A 2D slice can be reoriented such that the line lies in the slice. The user can then further evaluate or refine the landmark points based on both surface and image information.

A method of generating a custom three-dimensional (3D) model of a patient bone from one or more 2D images is disclosed. The method includes obtaining a 2D image of a bone, optionally of a joint, and identifying a 3D bone template for a candidate or representative bone from a pre-aligned library of representative bones. The method further includes repositioning one or more views of the 3D model or 2D images (e.g., with respect to rotation angle or caudal angle). In an iterative process, another 3D bone model for another candidate bone can be identified based on the repositioning until an accuracy threshold is satisfied. When the accuracy threshold is satisfied, surface region(s) of the current 3D bone model can then be modified to generate the resulting 3D model for the patient bone. The process can then be repeated for other bone(s) associated with the joint of the patient.