US 12,236,599 B2
Systems and methods for analyzing, detecting, and treating fibrotic connective tissue network formation
Shamik Mascharak, Santa Cruz, CA (US); Heather E. Talbott, Portola Valley, CA (US); Mimi Borrelli, Stanford, CA (US); Alessandra Laura Moore, Stanford, CA (US); and Michael T. Longaker, Stanford, CA (US)
Assigned to The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
Appl. No. 17/597,833
Filed by The Board of Trustees of the Leland Stanford Junior University, Stanford, CA (US)
PCT Filed Jul. 27, 2020, PCT No. PCT/US2020/043717
§ 371(c)(1), (2) Date Jan. 25, 2022,
PCT Pub. No. WO2021/021720, PCT Pub. Date Feb. 4, 2021.
Claims priority of provisional application 62/879,366, filed on Jul. 26, 2019.
Prior Publication US 2022/0261996 A1, Aug. 18, 2022
Int. Cl. G06T 7/00 (2017.01); G06T 5/70 (2024.01); G06T 7/13 (2017.01); G06T 7/90 (2017.01); A61B 5/00 (2006.01)
CPC G06T 7/0014 (2013.01) [G06T 5/70 (2024.01); G06T 7/13 (2017.01); G06T 7/90 (2017.01); A61B 5/444 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A method for tissue analysis comprising:
obtaining a set of one or more tissue sample images taken of a patient, wherein the set of tissue sample images comprises tissue stained with Masson's Trichrome, Picrosirius Red, or reticulin silver;
processing, using a computational system, the set of tissue sample images such that a fiber network can be identified and quantified within each tissue sample image, wherein processing comprises performing color deconvolution of the set of tissue sample images, wherein at least one tissue sample image of the set comprises Picrosirius red stain of the tissue, and wherein performing color deconvolution comprises reducing the Picrosirius red stain to red mature fibers and green immature fibers;
evaluating, using the computational system, the set of processed sample images against a set of parameters wherein the parameters of the set of parameters correspond to fiber characteristics, wherein the parameters of the set of parameters are quantified from the set of processed sample images and wherein the quantified parameters are weighted against a known set of fiber characteristics for establishing a level of fibrosis; and
assigning, using the computational system, a fibrotic tissue score to the set of tissue sample images that is representative of the level of fibrosis, wherein the fibrotic tissue score is a number within a continuous range.