New image‐guided method for localisation of an active capsule endoscope in the stomach

Aghanouri, Mehrnaz; Ghaffari, Ali; Serej, Nasim Dadashi; Rabbani, Hossein; Adibi, Peyman

doi:10.1049/iet-ipr.2018.6366

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Slightly older work includes [23], which computes a two-dimensional visibility of the colon; [24] which is an offline (non-real time) technique that uses classical techniques based on Shape from Shading and Shape from Motion to produce a dense 3D reconstruction; and [25], which bases its colon surface reconstruction on the geometry of the Haustral ridges. Finally, we mention a trio of works [26]- [28] whose purpose is to compute pure visual odometry (i.e. pose) without regard to either depth estimation or coverage.…”

Section: Related Workmentioning

confidence: 99%

Detecting Deficient Coverage in Colonoscopies

Freedman

Blau

Katzir

et al. 2020

IEEE Trans. Med. Imaging

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Colonoscopy is tool of choice for preventing Colorectal Cancer, by detecting and removing polyps before they become cancerous. However, colonoscopy is hampered by the fact that endoscopists routinely miss 22-28% of polyps. While some of these missed polyps appear in the endoscopist's field of view, others are missed simply because of substandard coverage of the procedure, i.e. not all of the colon is seen. This paper attempts to rectify the problem of substandard coverage in colonoscopy through the introduction of the C2D2 (Colonoscopy Coverage Deficiency via Depth) algorithm which detects deficient coverage, and can thereby alert the endoscopist to revisit a given area. More specifically, C2D2 consists of two separate algorithms: the first performs depth estimation of the colon given an ordinary RGB video stream; while the second computes coverage given these depth estimates. Rather than compute coverage for the entire colon, our algorithm computes coverage locally, on a segment-bysegment basis; C2D2 can then indicate in real-time whether a particular area of the colon has suffered from deficient coverage, and if so the endoscopist can return to that area. Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies. The C2D2 algorithm achieves state of the art results in the detection of deficient coverage. On synthetic sequences with ground truth, it is 2.4 times more accurate than human experts; while on real sequences, C2D2 achieves a 93.0% agreement with experts.

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Section: Related Workmentioning

confidence: 99%

Detecting Deficient Coverage in Colonoscopies

Freedman

Blau

Katzir

et al. 2020

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…Slightly older work includes [23], which computes a two-dimensional visibility of the colon; [24] which is an offline (non-real time) technique that uses classical techniques based on Shape from Shading and Shape from Motion to produce a dense 3D reconstruction; and [25], which bases its colon surface reconstruction on the geometry of the Haustral ridges. Finally, we mention a trio of works [26], [27], [28] whose purpose is to compute pure visual odometry (i.e. pose) without regard to either depth estimation or coverage.…”

Section: Related Workmentioning

confidence: 99%

Detecting Deficient Coverage in Colonoscopies

Freedman¹,

Blau²,

Katzir³

et al. 2020

Preprint

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Colonoscopy is the tool of choice for preventing Colorectal Cancer, by detecting and removing polyps before they become cancerous. However, colonoscopy is hampered by the fact that endoscopists routinely miss 22-28% of polyps. While some of these missed polyps appear in the endoscopist's field of view, others are missed simply because of substandard coverage of the procedure, i.e. not all of the colon is seen. This paper attempts to rectify the problem of substandard coverage in colonoscopy through the introduction of the C2D2 (Colonoscopy Coverage Deficiency via Depth) algorithm which detects deficient coverage, and can thereby alert the endoscopist to revisit a given area. More specifically, C2D2 consists of two separate algorithms: the first performs depth estimation of the colon given an ordinary RGB video stream; while the second computes coverage given these depth estimates. Rather than compute coverage for the entire colon, our algorithm computes coverage locally, on a segment-by-segment basis; C2D2 can then indicate in real-time whether a particular area of the colon has suffered from deficient coverage, and if so the endoscopist can return to that area. Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies. The C2D2 algorithm achieves state of the art results in the detection of deficient coverage. On synthetic sequences with ground truth, it is 2.4 times more accurate than human experts; while on real sequences, C2D2 achieves a 93.0% agreement with experts.

show abstract