Accurate-BV automatically detects and characterizes vascular networks in 3D medical images. This solution was designed for assessing perforating blood vessels in the context of autologous reconstructions such as DIEP and ALT, helping radiologists and plastic surgeons at identifying the most suitable tissue flap.
The optimized image processing algorithm underlying Accurate-BV tracks and measure with a voxel-level precision clinically relevant features of blood vessels (e.g. calibre, intramuscular length) that cross different tissues and anatomical structures.
Challenge | Opportunity
The incoherencies between the imaging studies and the surgical findings presented by the current manual method implicate higher healthcare costs. Solutions based on 3D imaging software have been tested in DIEP flap planning, but they fail to provide precise information on perforator location and properties, which is critical for a thorough surgical planning. Moreover, conventional methodologies for blood vessel segmentation have a poor performance in tracking blood vessels perforators, especially in the intramuscular portion where the signal-to-noise ratio is low.
- Reduce DIEP flap surgery and recovery duration, as well as overall complications;
- Increase the quality of breast reconstruction and patients’ satisfaction;
- Avoid surgery rescheduling due to incorrect preoperative assessment of blood vessels
- Support analysis of perforator blood vessels, reducing radiologists’ workload and easing the tracking of blood vessels in regions of difficult evaluation (i.e., intramuscular).
- Preoperative planning of DIEP flap surgery
- Track and analyse tubular structures in large series of two-dimensional medical images
Development StageLab prototype (TRL 3-4)
Further Information<p><a href="https://worldwide.espacenet.com/publicationDetails/biblio?CC=EP&NR=3352135B1&KC=B1&FT=D&ND=2&date=20190911&DB=EPODOC&locale=en_EP">EP3352135 (B1)</a></p> <p><a href="https://worldwide.espacenet.com/publicationDetails/biblio?CC=CN&NR=108324300A&KC=A&FT=D&ND=2&date=20180727&DB=EPODOC&locale=en_EP">CN108324300 (A)</a></p> <p><a href="https://worldwide.espacenet.com/publicationDetails/biblio?CC=JP&NR=2018134393A&KC=A&FT=D&ND=2&date=20180830&DB=EPODOC&locale=en_EP">JP2018134393 (A)</a></p> <p><a href="https://worldwide.espacenet.com/publicationDetails/biblio?CC=US&NR=2018199997A1&KC=A1&FT=D&ND=3&date=20180719&DB=EPODOC&locale=en_EP">US2018199997 (A1)</a></p> <p> </p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0895611118305810">Computer Aided Detection of Deep Inferior Epigastric Perforators in Computed Tomography Angiography scans</a></p> <p><a href="https://www.sciencedirect.com/science/article/pii/S0895611118305810">Computerized Medical Imaging and Graphics (2019)</a></p>
IPR StatusPatent Granted: Europe | Patent Pending: China, Japan and USA
TagsMedical imaging, e-Health, Computer-aided diagnosis, Software