IP Library › Granted Patent US 12,414,748
Granted Patent B2
US 12,414,748 · App. 18/435,386 · Granted Sep 16, 2025

Systems and methods for automated and interactive analysis of bone scan images for detection of metastases

Inventors: Karl Vilhelm Sjöstrand (Atlantic Highlands, NJ); Jens Filip Andreas Richter (Staffanstorp, SE); Lars Edenbrandt (Lund, SE)
Assignees: Progenics Pharmaceuticals, Inc.; EXINI Diagnostics AB
A61B6/469A61B6/037A61B6/465A61B6/505A61B6/5217A61B6/5258A61B6/563A61K51/0489G06T7/0012G06T7/11G06T7/73G16H15/00G16H30/20G16H30/40G16H50/20G16H50/30G16H50/70G16H70/60G06T2200/24G06T2207/30008G06T2207/30096
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Quick Facts
Patent No.
US 12,414,748
App. No.
18/435,386
Filed
Feb 7, 2024
Granted
Sep 16, 2025
Kind
B2
Art Unit
2672
USPC
600/431
Abstract

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

Claims (79)

1. A method for lesion marking and quantitative analysis of nuclear medicine images of a human subject, the method comprising:

(a) accessing, by a processor of a computing device, a bone scan image set for the human subject, said bone scan image set obtained following administration of an agent to the human subject;

(b) automatically segmenting, by the processor, each image in the bone scan image set to identify one or more skeletal regions of interest, each corresponding to a particular anatomical region of a skeleton of the human subject, thereby obtaining an annotated set of images, wherein the one or more skeletal regions of interest comprise at least one of (i) and (ii):

(i) a femur region corresponding to a portion of a femur of the human subject; and

(ii) a humerus region corresponding to a portion of a humerus of the human subject;

(c) automatically detecting, by the processor, an initial set of one or more hotspots, each hotspot corresponding to an area of elevated intensity in the annotated set of images, said automatically detecting comprising identifying the one or more hotspots using intensities of pixels in the annotated set of images and using one or more region-dependent threshold values, and wherein the one or more region dependent threshold values include one or more values associated with the femur region and/or the humerus region that provide enhanced hotspot detection sensitivity in the femur region and/or the humerus region to compensate for reduced uptake of the agent therein;

(d) for each hotspot in the initial set of hotspots, extracting, by the processor, a set of hotspot features associated with the hotspot;

(e) for each hotspot in the initial set of hotspots, calculating, by the processor, a metastasis likelihood value corresponding to a likelihood of the hotspot representing a metastasis, based on the set of hotspot features associated with the hotspot; and

(f) causing, by the processor, rendering of a graphical representation of at least a portion of the initial set of hotspots for display within a graphical user interface (GUI),

wherein step (b) comprises:

comparing each member of the bone scan image set with a corresponding atlas image of an atlas image set, each atlas image comprising one or more identifications of the one or more skeletal regions of interest, said skeletal regions of interest including (i) the femur region, comprising at least a portion of a knee region of the human subject, and/or (ii) the humerus region, comprising at least a portion of an elbow region of the human subject; and

for each image of the bone scan image set, registering the corresponding atlas image with the image of the bone scan image set using the identified knee region and/or the identified elbow region in the image as (a) landmark(s), such that the identifications of the one or more skeletal regions of interest of the atlas image are applied to the image of the bone scan image set.

2. The method of claim 1 , wherein a location of at least one detected hotspot of the initial hotspot set corresponds to a physical location in or on a femur more than three quarters of a distance along the femur from an end of the femur oriented toward a hip of the human subject to an end of the femur oriented toward a knee of the human subject.

3. The method of claim 1 , wherein a location of at least one detected hotspot of the initial hotspot set corresponds to a physical location in or on a humerus more than three quarters of a distance along the humerus from an end of the humerus oriented toward a shoulder of the human subject to an end of the humerus oriented toward an elbow of the human subject.

4. The method of claim 1 , where step (c) comprises:

identifying, by the processor, healthy tissue regions in the images of the bone scan image set determined not to include any hotspots;

calculating, by the processor, a normalization factor such that a product of the normalization factor and an average intensity of the identified healthy tissue regions is a pre-defined intensity level; and

normalizing, by the processor, the images of the bone scan image set by the normalization factor.

5. The method of claim 1 , further comprising:

(g) calculating, by the processor, one or more risk index values for the human subject based at least in part on a computed fraction of the skeleton of the human subject occupied by the initial set of hotspots.

6. The method of claim 5 , wherein at least one of the risk index values is indicative of a risk of the human subject having and/or developing metastatic cancer.

7. The method of claim 6 , wherein the metastatic cancer is metastatic prostate cancer.

8. The method of claim 5 , wherein at least one of the risk index values is indicative of the human subject having a particular state of metastatic cancer.

9. The method of claim 1 , comprising:

(h) selecting, by the processor, a first subset of the initial set of hotspots based at least in part on the metastasis likelihood values; and

(i) causing, by the processor, rendering of a graphical representation of the first subset for display within a graphical user interface (GUI).

10. The method of claim 9 , further comprising:

(j) calculating, by the processor, one or more risk index values for the human subject based at least in part on a computed fraction of the skeleton of the human subject occupied by the first subset of hotspots.

11. The method of claim 1 , comprising:

(k) receiving, by the processor, via the GUI, a user selection of a second subset of the initial set of hotspots; and

(l) calculating, by the processor, one or more risk index values for the human subject based at least in part on a computed fraction of the skeleton of the human subject occupied by the second subset of hotspots.

12. The method of claim 1 , wherein the processor is a processor of a cloud-based system.

13. The method of claim 1 , wherein the GUI is part of a general Picture Archiving and Communications System (PACS).

14. The method of claim 1 , wherein the agent comprises technetium 99m methylenediphosphonate ( 99m Tc-MDP).

15. A system for lesion marking and quantitative analysis of nuclear medicine images of a human subject, the system comprising:

a processor; and

a memory having instructions thereon, wherein the instructions, when executed by the processor, cause the processor to:

(a) access a bone scan image set for the human subject, said bone scan image set obtained following administration of an agent to the human subject;

(b) automatically segment each image in the bone scan image set to identify one or more skeletal regions of interest, each corresponding to a particular anatomical region of a skeleton of the human subject, thereby obtaining an annotated set of images, wherein the one or more skeletal regions of interest comprise at least one of (i) and (ii):

(i) a femur region corresponding to a portion of a femur of the human subject; and

(ii) a humerus region corresponding to a portion of a humerus of the human subject;

(c) automatically detect an initial set of one or more hotspots, each hotspot corresponding to an area of elevated intensity in the annotated set of images, said automatically detecting comprising identifying the one or more hotspots using intensities of pixels in the annotated set of images and using one or more region-dependent threshold values, and wherein the one or more region dependent threshold values include one or more values associated with the femur region and/or the humerus region that provide enhanced hotspot detection sensitivity in the femur region and/or the humerus region to compensate for reduced uptake of the agent therein;

(d) for each hotspot in the initial set of hotspots, extract a set of hotspot features associated with the hotspot;

(e) for each hotspot in the initial set of hotspots, calculate a metastasis likelihood value corresponding to a likelihood of the hotspot representing a metastasis, based on the set of hotspot features associated with the hotspot; and

(f) cause rendering of a graphical representation of at least a portion of the initial set of hotspots for display within a graphical user interface (GUI),

wherein step (b) comprises:

comparing each member of the bone scan image set with a corresponding atlas image of an atlas image set, each atlas image comprising one or more identifications of the one or more skeletal regions of interest, said skeletal regions of interest including (i) the femur region, comprising at least a portion of a knee region of the human subject, and/or (ii) the humerus region, comprising at least a portion of an elbow region of the human subject; and

for each image of the bone scan image set, registering the corresponding atlas image with the image of the bone scan image set using the identified knee region and/or the identified elbow region in the image as (a) landmark(s), such that the identifications of the one or more skeletal regions of interest of the atlas image are applied to the image of the bone scan image set.

16. A computer aided image analysis device comprising the system of claim 15 .

17. The device of claim 16 , wherein the device is programmed to be used by trained healthcare professionals and/or researchers.

18. The device of claim 17 , wherein the device is programmed to be used for analysis of bone scan images for evaluation and/or detection of metastatic cancer.

19. The device of claim 17 , wherein the device is programmed to be used for analysis of bone scan images for evaluation and/or detection of prostate cancer.

20. The device of claim 16 , comprising a label specifying that the device is intended to be used by trained healthcare professionals and/or researchers.

21. The device of claim 20 , wherein the label further specifies that the device is intended to be used for analysis of bone scan images for evaluation and/or detection of metastatic cancer.

22. The device of claim 20 , wherein the label further specifies that the device is intended to be used for analysis of bone scan images for evaluation and/or detection of prostate cancer.

23. A method for lesion marking and quantitative analysis of nuclear medicine images of a human subject, the method comprising:

(a) accessing, by a processor of a computing device, a bone scan image set for the human subject, said bone scan image set obtained following administration of an agent to the human subject;

(b) automatically segmenting, by the processor, each image in the bone scan image set to identify one or more skeletal regions of interest, each corresponding to a particular anatomical region of a skeleton of the human subject, thereby obtaining an annotated set of images, wherein the one or more skeletal regions of interest comprise at least one of (i) and (ii):

(i) a femur region corresponding to a portion of a femur of the human subject; and

(ii) a humerus region corresponding to a portion of a humerus of the human subject;

(c) automatically detecting, by the processor, an initial set of one or more hotspots, each hotspot corresponding to an area of elevated intensity in the annotated set of images, said automatically detecting comprising identifying the one or more hotspots using intensities of pixels in the annotated set of images and using one or more region-dependent threshold values, and wherein the one or more region dependent threshold values include one or more values associated with the femur region and/or the humerus region that provide enhanced hotspot detection sensitivity in the femur region and/or the humerus region to compensate for reduced uptake of the agent therein;

(d) for each hotspot in the initial set of hotspots, extracting, by the processor, a set of hotspot features associated with the hotspot;

(e) for each hotspot in the initial set of hotspots, calculating, by the processor, a metastasis likelihood value corresponding to a likelihood of the hotspot representing a metastasis, based on the set of hotspot features associated with the hotspot;

(f) causing, by the processor, rendering of a graphical representation of at least a portion of the initial set of hotspots for display within a graphical user interface (GUI);

(g) receiving, by the processor, via the GUI, a user selection of a second subset of the initial set of hotspots; and

(h) calculating, by the processor, one or more risk index values for the human subject based at least in part on a computed fraction of the skeleton of the human subject occupied by the second subset of hotspots.

24. A system for lesion marking and quantitative analysis of nuclear medicine images of a human subject, the system comprising:

a processor; and

a memory having instructions thereon, wherein the instructions, when executed by the processor, cause the processor to:

(a) access a bone scan image set for the human subject, said bone scan image set obtained following administration of an agent to the human subject;

(b) automatically segment each image in the bone scan image set to identify one or more skeletal regions of interest, each corresponding to a particular anatomical region of a skeleton of the human subject, thereby obtaining an annotated set of images, wherein the one or more skeletal regions of interest comprise at least one of (i) and (ii):

(i) a femur region corresponding to a portion of a femur of the human subject; and

(ii) a humerus region corresponding to a portion of a humerus of the human subject;

(c) automatically detect an initial set of one or more hotspots, each hotspot corresponding to an area of elevated intensity in the annotated set of images, said automatically detecting comprising identifying the one or more hotspots using intensities of pixels in the annotated set of images and using one or more region-dependent threshold values, and wherein the one or more region dependent threshold values include one or more values associated with the femur region and/or the humerus region that provide enhanced hotspot detection sensitivity in the femur region and/or the humerus region to compensate for reduced uptake of the agent therein;

(d) for each hotspot in the initial set of hotspots, extract a set of hotspot features associated with the hotspot;

(e) for each hotspot in the initial set of hotspots, calculate a metastasis likelihood value corresponding to a likelihood of the hotspot representing a metastasis, based on the set of hotspot features associated with the hotspot;

(f) cause rendering of a graphical representation of at least a portion of the initial set of hotspots for display within a graphical user interface (GUI);

(g) receiving, by the processor, via the GUI, a user selection of a second subset of the initial set of hotspots; and

(h) calculating, by the processor, one or more risk index values for the human subject based at least in part on a computed fraction of the skeleton of the human subject occupied by the second subset of hotspots.

Assignments (3)
SECURITY INTEREST Recorded Dec 17, 2025
From: LANTHEUS MEDICAL IMAGING, INC.; PROGENICS PHARMACEUTICALS, INC.; MOLECULAR INSIGHT PHARMACEUTICALS, INC.
To: CITIZENS BANK, N.A., AS COLLATERAL AGENT, NATIONAL BANKING ASSOCIATION
Reel/Frame 073250/0201 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 10, 2024
From: RICHTER, JENS FILIP ANDREAS; EDENBRANDT, LARS
To: EXINI DIAGNOSTICS AB
Reel/Frame 067376/0708 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 10, 2024
From: SJÖSTRAND, KARL VILHELM
To: PROGENICS PHARMACEUTICALS, INC.
Reel/Frame 067376/0717 →
Continuity (4)
Continuation 17989863 · Nov 18, 2022
Continuation 16856120 · Apr 23, 2020
Provisional Application 62837955 · Apr 24, 2019
Related Publication 20240285246A1 · Aug 29, 2024
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