How big is this neoplasia? Live colonoscopic size measurement using the Infocus-Breakpoint

• A new paradigm: image based colonoscopic size measurement.
• A technical solution using regular colonoscopic images.
• The notion of Infocus-Breakpoint, its use to estimate scene scale from images.
• A robust implementation including simultaneous tracking and optical blur recovery.
• An interactive system usable in the OR with an accuracy of less than 1 mm.

Colonoscopy is the reference medical examination for early diagnosis and treatment of colonic diseases. This minimally invasive technique allows endoscopists to explore the colon cavity and remove neoplasias – abnormal growths of tissue – which may develop into malignant tumors. The size, shape and appearance of a neoplasia are essential cues for diagnostic. However, the size is difficult to estimate because the absolute scale of the observed tissue is not directly conveyed in the 2D colonoscopic images. An erroneous size estimate may lead to inappropriate treatment. There currently exist no solutions to reproducible neoplasia size measurement adapted to colonoscopy.We propose a colonoscopic size measurement system for neoplasias. By using a simple planar geometry, the key technical problem is reduced to resolving scale. Our core contribution is introducing the Infocus-Breakpoint (IB) that allows us to resolve scale from a regular colonoscopic video. We define the IB as the lower limit of the colonoscope’s depth of field. The IB corresponds to a precise colonoscope to tissue distance, called the reference depth, which we calibrate preoperatively. We detect the IB intraoperatively thanks to two novel modules: deformable Blur-Estimating Tracking (BET) and Blur-Model Fitting (BMF). With our system, the endoscopist may interactively measure the length and area of a neoplasia in a 2D colonoscopic image directly. Our system needs no hardware modification to standard monocular colonoscopes, yet reaching a size measurement accuracy of the order of a millimeter, as shown on several phantom and patient datasets.

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