Automatic measurement of lesions on medical images

• US 9,830,699 B2
• Filed: 02/28/2014
• Issued: 11/28/2017
• Est. Priority Date: 03/01/2013
• Status: Active Grant

• Current Assignee: Centre Hospitalier Universitaire d'Angers, Universite D'Angers
• Original Assignee: Centre Hospitalier Universitaire d'Angers, Universite D'Angers
• Inventors: Cales, Paul, Chaigneau, Julien, Hunault, Gilles, Rousselet, Marie-Christine, Aube, Christophe
• Primary Examiner(s): Azarian, Seyed
• Application Number:
  US14/771,997
• Publication Number:
  US 20160012583A1
• Time in Patent Office: 1,369 Days
• Field of Search: 382100, 382103, 382106, 382128-134, 382154, 382162, 382168, 382173, 382181, 382190, 382193, 382199, 382203, 382209, 382219, 382224, 382232, 382254, 382274, 382276, 382291, 382305, 382312, 702 21, 435 611, 707769, 424464, 424 856
• CPC Class Codes: G06T 2207/10056 Microscopic image
• CPC Class Codes: G06T 2207/30056 Liver; Hepatic
• CPC Class Codes: G06T 2207/30096 Tumor; Lesion
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/90 Determination of colour characteristics

Abstract

The present invention relates to a method for assessing the presence and/or the severity of a lesion in an organ or tissue of a subject through automated analysis of at least one image of said organ or tissue, wherein said organ or tissue is preferably a liver organ or liver tissue, comprising the calculation of a score combining descriptors of said image, wherein said method comprises the steps of: a. measuring on said at least one image at least two descriptors of said at least one image; b. mathematically combining said at least two descriptors in a score; and c. assessing the presence and/or the severity of a lesion in the organ or tissue based on the value of the score calculated at step (b).

Claims

1. A non-invasive method for assessing the presence of a clinically significant fibrosis in a liver organ or liver tissue of a subject through automated analysis of at least one image of said liver organ or liver tissue, the method utilizing a microprocessor comprising a computer algorithm configured to calculate a score combining descriptors of said image, said method comprising the steps of:

a. measuring on said at least one image at least two descriptors of said at least one image, the descriptors selected from the group consisting of;

linearity percentage of the edges, mean of percentage of fibrosis around areas, area of stellar fibrosis among the total surface of the liver biopsy specimen, number of bridges, bridges thickness, mean area of porto-septal regions, bridges perimeter, ratio of bridges among the porto-septal areas, area of fibrosis in the bridges, fractal dimension of peri-sinusoidal fibrosis, perimeter of the liver organ, liver tissue or fragment thereof, fractal dimension of porto-septal fibrosis, ratio of peri-sinusoidal fibrosis among the whole fibrosis, length of the liver organ, liver tissue or fragment thereof, anfractuosity descriptors selected from native perimeter, smoothed perimeter and ratio between both perimeters, fractal dimension of fibrosis, interquartile range of total density, Arantius furrow thickness, mean native liver perimeter, mean total spleen perimeter, ratio spleen surface to liver surface, and mathematic combination thereof;

b. mathematically combining said at least two measured descriptors from step (a) in to a score, wherein said mathematical combination is a binary logistic regression; and

c. assessing the presence of a clinically significant fibrosis, based on the value of the score calculated at step (b).

2. The method according to claim 1, wherein the at least one image is a histological section image, or an image of the entire liver organ or liver tissue.

3. The method according to claim 1, wherein the at least one image is obtained by an optical technique selected from the group consisting of:

microscopic physical imaging, second harmonic generation (SHG), multiphoton imaging, coherent anti-Stokes Raman scattering (CARS), two-photon excitation fluorescence (TPEF), diffuse optical imaging and event-related optical signal;

or by a non-optical technique selected from the group consisting of;

radiography, nuclear medicine, photoacoustic methods, and thermal methods.

4. The method according to claim 1, further comprising measuring at least one non-invasive test marker, measuring liver stiffness, or measuring both, in the subject and combining the resulting measure(s) with the measures of the two descriptors obtained at step (a).

5. The method according to claim 4, wherein the at least one non-invasive test marker is selected from the group consisting of a biomarker, a clinical data, a physical data, and a score selected from Enhanced Liver Fibrosis test (ELF™

) FibroSpect™

, AST to Platelet Ratio Index (APRI), Fibrosis-4 (FIB-4), Hepascore, Fibrotest™

, FibroMeter™

, CirrhoMeter™

, CombiMeter™

, and InflaMeter™

.

6. The method according to claim 1, wherein the clinically significant fibrosis is proto-septal fibrosis.

7. A non-invasive method for assessing the presence of cirrhosis in a subject through automated analysis of at least one image of liver organ or liver tissue from the subject, the method utilizing a microprocessor comprising a computer algorithm configured to calculate a score combining descriptors of said image, said method comprising the steps of:

a. measuring on said at least one image at least two descriptors of said at least one image, the descriptors selected from the group consisting of;

linearity percentage of the edges, granularity percentage, fragmentation, mean of percentage of fibrosis around areas, area of stellar fibrosis among the surface of porto-septal regions, portal distance, length of the liver biopsy, fractal dimension of porto-septal fibrosis, number of bridges, fractal dimension of peri-sinusoidal fibrosis, perimeter of the liver organ, liver tissue or fragment thereof, standard deviation of total density, second antero-posterior segment I length (D3), Arantius furrow thickness, ratio between spleen surface and liver surface, total spleen perimeter, portal furrow thickness and mathematic combination thereof;

b. mathematically combining said at least two measured descriptors from step (a) in to a score, wherein said mathematical combination is a binary logistic regression; and

c. assessing the presence of a cirrhosis, based on the value of the score calculated at step (b).

8. The method according to claim 7, wherein the at least one image is a histological section image, or an image of the entire liver organ or liver tissue.

9. The method according to claim 7, wherein the at least one image is obtained by an optical technique selected from the group consisting of:

microscopic physical imaging, second harmonic generation (SHG), multiphoton imaging, coherent anti-Stokes Raman scattering (CARS), two-photon excitation fluorescence (TPEF), diffuse optical imaging and event-related optical signal;

or by a non-optical technique selected from the group consisting of;

radiography, nuclear medicine, photoacoustic methods, and thermal methods.

10. The method according to claim 7, further comprising measuring at least one non-invasive test marker selected from the group consisting of a biomarker, a clinical data, a physical data and a score selected from Enhanced Liver Fibrosis test (ELF™

), FibroSpect™

, AST to Platelet Ratio Index (APRI), Fibrosis-4 (FIB-4), Hepascore, Fibrotest™

, FibroMeter™

, CirrhoMeter™

, CombiMeter™

, InflaMeter™

, and liver stiffness, and combining the resulting measure(s) with the measures of the two descriptors obtained at step (a).

11. A non-invasive method for determining an increased risk of liver complications, an increased risk of mortality, or both, through automated analysis of at least one image of liver organ or liver tissue, the method utilizing a microprocessor comprising a computer algorithm configured to calculate a score combining descriptors of said image, wherein said method comprises the steps of:

a. measuring on said at least one image at least two descriptors of said at least one image, the descriptors selected from the group consisting of;

linearity percentage of the edges, granularity percentage, fragmentation, mean of percentage of fibrosis around areas, area of stellar fibrosis among the surface of porto-septal regions, area of stellar fibrosis among the surface of lobular regions, area of porto-septal fibrosis, portal distance, fractal dimension of the edges of the organ, tissue or fragment thereof, area of steatosis, mean intensity of the image on the blue component, number of nodules, number of bridges, mean bridge area, fractal dimension of steatosis, luminosity of the parenchyma staining in the green component, number or fragments, and mathematical combination thereof;

b. mathematically combining said at least two measured descriptors from step (a) in to a score, wherein said mathematical combination is a binary logistic regression, a multiple linear regression or any multivariate statistical analysis; and

c. determining an increased risk of liver complications, an increased risk of mortality, or both, based on the value of the score calculated at step (b).

12. The method according to claim 11, wherein the at least one image is a histological section image, or an image of the entire liver organ or liver tissue.

13. The method according to claim 11, wherein the at least one image is obtained by an optical technique selected from the group consisting of:

microscopic physical imaging, second harmonic generation (SHG), multiphoton imaging, coherent anti-Stokes Raman scattering (CARS), two-photon excitation fluorescence (TPEF), diffuse optical imaging and event-related optical signal;

or by a non-optical technique selected from the group consisting of;

radiography, nuclear medicine, photoacoustic methods, and thermal methods.

14. The method according to claim 11, further comprising measuring at least one non-invasive test marker and combining the resulting at least one measure with the measures of the two descriptors obtained at step (a), wherein the at least one non-invasive test marker is selected from the group consisting of a biomarker, a clinical data, a physical data, and/or a score selected from Enhanced Liver Fibrosis test (ELF™

) FibroSpect™

, AST to Platelet Ratio Index (APRI), Fibrosis-4 (FIB-4), Hepascore, Fibrotest™

, FibroMeter™

, CirrhoMeter™

, CombiMeter™

, and InflaMeter™

.

15. The method according to claim 11, further comprising measuring a binary non-invasive diagnostic test marker and combining the resulting measure with the measures of the two descriptors obtained at step (a).

16. The method according to claim 11, further comprising measuring a Metavir F score (FM score), an Ishak stage, or both, and combining the resulting measure with the measures of the two descriptors obtained at step (a).