A system for surgical mesh analysis includes a display screen, a processor, and a memory. The memory has instructions stored thereon, which when executed by the processor, cause the system to: access an image of a surgical site (602). The image includes the surgical mesh. The surgical mesh includes a grid of cells. The instructions, when executed by the processor further cause the system to detect a first desired location on the surgical mesh in the image (604); detect a second desired location on the surgical mesh in the image (606); determine a distance between the first desired location and the second desired location along the surgical mesh in the image (608); and display, on the display screen, the determined distance (610).

A spatial orientation determining system includes an imaging device configured for obtaining an image of a surgical site, a surgical tool defining a central axis, a processor, and a memory. The surgical tool configured for operating at the surgical site and disposed thereon, a fiducial marker generated by a machine learning network. The fiducial marker includes a distinct pattern. The memory, includes instructions which when executed by the processor, cause the system to: access an image from the imaging device, the image including a portion of the fiducial marker; determine a spatial positioning of the surgical tool based on at least a visible portion of the fiducial marker and the distinct pattern; and determine, based on the spatial positioning, a positioning, an orientation, and/or a rotational angle of the surgical tool.

Methods, systems, and computer-readable media for detecting image degradation during a surgical procedure are provided. A method includes receiving images of a surgical instrument; obtaining baseline images of an edge of the surgical instrument; comparing a characteristic of the images of the surgical instrument to a characteristic of the baseline images of the edge of the surgical instrument, the images of the surgical instrument being received subsequent to obtaining the baseline images of the edge of the surgical instrument and being received while the surgical instrument is disposed at a surgical site in a patient; determining whether the images of the surgical instrument are degraded, based on the comparing of the characteristic of the images of the surgical instrument and the characteristic of the baseline images of the surgical instrument; and generating an image degradation notification, in response to a determination that the images of the surgical instrument are degraded.

The image processing apparatus acquires a plurality of types of candidate images based on an endoscope image, performs control of displaying, on a display, a display image based on at least one type of candidate image, performs a first analysis process on one or the plurality of types of candidate images set in advance, selects at least one type of candidate image from the plurality of types of candidate images as an optimum image based on a first analysis process result obtained through the first analysis process, and obtains a second analysis process result by performing a second analysis process on the optimum image.

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.

An image recording system includes a processor. The processor acquires a time series RAW image group including a plurality of time series RAW images in a first time section. The processor extracts, from the time series RAW image group, a recording candidate RAW image group included in a second time section as a part of the first time section. The processor records at least one RAW image included in the recording candidate RAW image group as a recording target RAW image which is a RAW image to be recorded. The processor selects the recording target RAW image from the recording candidate RAW image group. The processor converts the RAW image which is not selected as the recording target RAW image from the recording candidate RAW image group or the time series RAW image group to compressed data, and records the compressed data.

A surface estimation method includes a region-setting step and an estimation step. In the region-setting step, a reference region that is one of a three-dimensional region and a two-dimensional region is set. The three-dimensional region includes three or more points and is set in a three-dimensional space. The three-dimensional space includes three-dimensional coordinates of three or more points on a subject calculated on the basis of a two-dimensional image of the subject. The three-dimensional coordinates of the three or more points are included in three-dimensional image data. The two-dimensional region includes three or more points and is set in the two-dimensional image. In the estimation step, a reference surface that approximates a surface of the subject is estimated on the basis of three or more points of the three-dimensional image data corresponding to the three or more points included in the reference region.

Registration of a surgical image acquisition device (e.g. an endoscope) using preoperative and live contour signatures of an anatomical object is described. A control unit includes a processor configured to compare the real-time contour signature to the database of preoperative contour signatures of the anatomical object to generate a group of potential contour signature matches for selection of a final contour match. Registration of an image acquisition device to the surgical site is realized based upon an orientation corresponding to the selected final contour signature match.

An image processing apparatus includes a processor including hardware, the processor being configured to: estimate, based on image information from a medical device that includes at least an endoscope, a plurality of procedure actions of an operator of the endoscope; perform different supports respectively for procedures by the operator, according to an estimation result of the procedure actions; and output a display for the supports to a display.

Provided is a capsule endoscope. The capsule endoscope includes: an imaging device configured to perform imaging on a digestive tract in vivo to generate an image; an artificial neural network configured to determine whether there is a lesion area in the image; and a transmitter configured to transmit the image based on a determination result of the artificial neural network.