US 12,423,813 B2
System and method for machine-learning based sensor analysis and vascular tree segmentation
Moran Shalhon Livne, Rosh HaAyin (IL); Avishai Baruch Yaari, Hod-HaSharon (IL); Hila Blecher Segev, Ramat Ishai (IL); Tomer Shapira, Ra'anana (IL); and Ori Ahron Noked Partouche, Ramat Gan (IL)
Assigned to Cathworks Ltd., Kfar Saba (IL)
Filed by Cathworks Ltd., Kfar Saba (IL)
Filed on Sep. 28, 2023, as Appl. No. 18/477,466.
Application 18/477,466 is a continuation of application No. PCT/IB2023/051186, filed on Feb. 9, 2023.
Claims priority of provisional application 63/308,550, filed on Feb. 10, 2022.
Prior Publication US 2024/0029259 A1, Jan. 25, 2024
Int. Cl. G06V 10/764 (2022.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/33 (2017.01); G06T 7/62 (2017.01)
CPC G06T 7/0012 (2013.01) [G06T 7/0016 (2013.01); G06T 7/11 (2017.01); G06T 7/33 (2017.01); G06T 7/62 (2017.01); G06V 10/764 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01); G06T 2207/30101 (2013.01); G06T 2207/30168 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A method of identifying vascular portions of a vascular image, the method comprising:
accessing the vascular image comprising a 2-D angiographic image;
applying a first ML-based vascular identifier to the vascular image to produce a first data structure identifying a path that extends along at least some vascular portions, wherein a vascular portion is an image location associated with part of a blood vessel depicted in the vascular image;
applying a second vascular identifier to the vascular image to produce a second data structure identifying one or more pixel masks, wherein the pixel masks comprise respective blobs of vascular portions;
wherein the path
includes some vascular portions not in the one or more pixels masks; and
combining the first and second data structures to produce a combined identification of vascular portions that form a particular blood vessel depicted in the vascular image, wherein combining is based on:
connection information between the one or more pixel masks and the path, or
similarity of directionality between an individual pixel mask and the path.