US 11,657,508 B2
Systems and methods for platform agnostic whole body image segmentation
Jens Filip Andreas Richter, Lund (SE); Kerstin Elsa Maria Johnsson, Lund (SE); Erik Konrad Gjertsson, Lund (SE); and Aseem Undvall Anand, Queens, NY (US)
Assigned to EXINI Diagnostics AB, Lund (SE)
Filed by EXINI Diagnostics AB, Lund (SE)
Filed on Jan. 6, 2020, as Appl. No. 16/734,599.
Claims priority of provisional application 62/934,305, filed on Nov. 12, 2019.
Claims priority of provisional application 62/907,158, filed on Sep. 27, 2019.
Claims priority of provisional application 62/870,210, filed on Jul. 3, 2019.
Claims priority of provisional application 62/863,608, filed on Jun. 19, 2019.
Claims priority of provisional application 62/837,941, filed on Apr. 24, 2019.
Claims priority of provisional application 62/789,155, filed on Jan. 7, 2019.
Prior Publication US 2020/0245960 A1, Aug. 6, 2020
Int. Cl. G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16H 50/50 (2018.01); G16H 30/20 (2018.01); G16H 50/30 (2018.01); G16H 50/20 (2018.01); G16H 30/40 (2018.01); A61B 6/03 (2006.01); A61B 6/00 (2006.01); A61K 51/04 (2006.01); G06V 20/64 (2022.01); G06V 20/69 (2022.01); G06V 30/24 (2022.01); G06F 18/214 (2023.01)
CPC G06T 7/11 (2017.01) [A61B 6/032 (2013.01); A61B 6/037 (2013.01); A61B 6/463 (2013.01); A61B 6/466 (2013.01); A61B 6/481 (2013.01); A61B 6/505 (2013.01); A61B 6/507 (2013.01); A61B 6/5205 (2013.01); A61B 6/5241 (2013.01); A61B 6/5247 (2013.01); A61K 51/0455 (2013.01); G06F 18/214 (2023.01); G06V 20/64 (2022.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06V 30/2504 (2022.01); G16H 30/20 (2018.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G16H 50/50 (2018.01); G06V 2201/031 (2022.01); G06V 2201/033 (2022.01)] 10 Claims
OG exemplary drawing
 
1. A method for automatically processing 3D images to identify, and measure uptake of radiopharmaceutical in, cancerous lesions within a subject having or at risk for a cancer, the method comprising:
(a) receiving, by a processor of a computing device, a 3D anatomical image of a subject obtained using an anatomical imaging modality, wherein the 3D anatomical image comprises a graphical representation of tissue within the subject;
(b) automatically identifying, by the processor, using one or more machine learning modules, within the 3D anatomical image:
a first skeletal volume comprising a graphical representation of one or more bones of the subject;
a first aorta volume comprising a graphical representation of at least a portion of an aorta of the subject; and
a first liver volume comprising a graphical representation of a liver of the subject;
(c) determining, by the processor, a 3D segmentation map representing a plurality of 3D segmentation masks, including a skeletal mask representing the identified first skeletal volume, an aorta mask representing the identified first aorta volume, and a liver mask representing the identified first liver volume;
(d) receiving, by the processor, a 3D functional image of the subject obtained using a functional imaging modality;
(e) automatically identifying, within the 3D functional image, using the 3D segmentation map:
a second skeletal volume corresponding to the first identified skeletal volume, within the 3D anatomical image;
a second aorta volume corresponding to the first aorta volume, identified within the 3D anatomical image; and
a second liver volume corresponding to the first liver volume, identified within the 3D anatomical image;
(f) automatically detecting, by the processor, within the second skeletal volume, one or more hotspots determined to represent lesions based on intensities of voxels within the second skeletal volume; and
(g) determining, by the processor, for each of the one or more detected hotspots, an individual hotspot index value by:
determining an aorta reference intensity level based on a measure of intensity of voxels within the second aorta volume;
determining a liver reference intensity level based on a measure of intensity of voxels within the second liver volume; and
for each individual detected hotspot:
determining a corresponding individual hotspot intensity level based on a measure of intensity of voxels of the detected hotspot; and
determining a corresponding individual hotspot index level from the individual hotspot intensity level, the aorta reference intensity level, and the liver reference intensity level.