Systems and methods for generating three-dimensional measurements using endoscopic video data

A system for tracking a location of a fixed point in endoscopic images, the system comprising:

• a memory;
• and
• one or more processors,
• wherein the memory stores one or more programs that, when executed by the one or more processors, cause the one or more processors to:
• receive video data captured by an endoscopic imaging device configured to image an internal area of a patient;
• receive a selection from a user of a location within a first two-dimensional image of the video data on which to place a graphical marker;
• place the graphical marker on the first two-dimensional image at the location within the first two-dimensional image;
• detect a first location of an interest point within the first two-dimensional image, wherein detecting the first location of an interest point comprises applying a machine learning model to the first two-dimensional image to identify the interest point;
• detect a second location of the interest point within a second two-dimensional image of the video data, wherein detecting the second location of the interest point comprises applying the machine learning model to the second two-dimensional image to identify the interest point;
• and
• place the graphical marker on the second two-dimensional image in accordance with the detection of the second location of the interest point.

A non-transitory computer readable storage medium storing one or more programs for execution by one or more processors of a computing system, the one or more programs including instructions that, when executed by the one or more processors, cause the computing system to:

• receive video data captured by an endoscopic imaging device configured to image an internal area of a patient;
• receive a selection from a user of a location within a first two-dimensional image of the video data on which to place a graphical marker;
• place the graphical marker on the first two-dimensional image at the location within the first two-dimensional image;
• detect a first location of an interest point within the first two-dimensional image, wherein detecting the first location of the interest point comprises applying a machine learning model to the first two-dimensional image to identify the interest point;
• detect a second location of the interest point within a second two-dimensional image of the video data, wherein detecting the second location of the interest point comprises applying the machine learning model to the second two-dimensional image to identify the interest point;
• and
• place the graphical marker on the second two-dimensional image in accordance with the detection of the second location of the interest point.