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US 11,937,962 B2Granted
Systems and methods for automated and interactive analysis of bone scan images for detection of metastases
AssigneesProgenics Pharmaceuticals, Inc., EXINI Diagnostics AB
InventorsKarl Vilhelm Sjöstrand, Jens Filip Andreas Richter, Lars Edenbrandt
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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.
Claims30
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 the one or more region dependent threshold values comprise (i) a first, reduced intensity, threshold value associated with the femur region and/or the humerus region and (ii) a second threshold value associated with one or more other skeletal regions of interest, wherein the first threshold value is lower than the second threshold value, thereby increasing detection sensitivity in the femur region and/or the humerus region.
8 more elements
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 the one or more region dependent threshold values comprise (i) a first, reduced intensity, threshold value associated with the femur region and/or the humerus region and (ii) a second threshold value associated with one or more other skeletal regions of interest, wherein the first threshold value is lower than the second threshold value, thereby increasing detection sensitivity in the femur region and/or the humerus region.
11 more elements
A computer aided image analysis device comprising a system for lesion marking and quantitative analysis of nuclear medicine images of a human subject and a label specifying that the device is intended to be used by trained healthcare professionals and/or researchers, wherein the system comprises:
- 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).
10 more elements
Dependencies are inferred from the claim text.