System for detecting bone cancer metastases

Abstract

The invention relates to a detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising a shape identifier unit, a hotspot detection unit, a hotspot feature extraction unit, a first artificial neural network unit, a patient feature extraction unit, and a second artificial neural network unit.

The invention claimed is: 
     
       1. A detection system for automatic detection of bone cancer metastases from a set of isotope bone scan images of a patients skeleton, the system comprising:
 a shape identifier unit for identifying anatomical structures of the skeleton pictured in the set of bone scan images, forming an annotated set of images; 
 a hotspot detection unit for detecting areas of high intensity in the annotated set of images based on information from the shape identifier regarding the anatomical structures corresponding to different portions of the skeleton of the images; 
 a hotspot feature extraction unit for extracting a set of hotspot features for each hot spot detected by the hotspot detection unit; 
 a first artificial neural network unit arranged to calculate a likelihood for each hot spot of the hotspot set being a metastasis based on the set of hotspot features extracted by the hotspot feature extraction unit; 
 a patient feature extraction unit arranged to extract a set of patient features based on the hotspots detected by the hotspot detection unit and on the likelihood outputs from the first artificial neural network unit; and 
 a second artificial neural network unit arranged to calculate a likelihood that the patient has one or more cancer metastases, based on the set of patient features extracted by the patient feature extraction unit; and 
 an input image memory, 
 wherein the shape identifier unit accesses the set of isotope bone scan images from the input image memory and wherein, upon extraction of the set of patient features, the patient feature extraction unit stores the set of patient features in a patient feature memory for accessing by the second artificial neural network to calculate the likelihood that the patient has one or more cancer metastases. 
 
     
     
       2. The detection system as recited in  claim 1 , wherein the shape identifier unit comprises a predefined skeleton model of a skeleton, the skeleton model comprising one or more anatomical regions, each region representing an anatomical portion of a general skeleton. 
     
     
       3. The detection system as recited in  claim 2 , wherein the predefined skeleton model is adjusted to match the skeleton of the set of bone scan images of the patient, forming a working skeleton model. 
     
     
       4. The detection system as recited in  claim 1 , wherein the hotspot detection unit comprises a threshold scanner unit for scanning the set of bone scan images and identifying pixels above a certain threshold level. 
     
     
       5. The detection system as recited in  claim 4 , wherein the hotspot detection unit comprises different threshold levels for the different anatomical regions that are defined by the shape identifier unit. 
     
     
       6. The detection system as recited in  claim 1 , wherein the hotspot feature extraction unit for extracting one or more hotspot features for each hot spot, comprises means for determining the shape and position of each hotspot. 
     
     
       7. The detection system as recited in  claim 1 , wherein the first artificial neural network unit are fed with the features of each hotspot of the hotspot set produced by the hotspot feature extraction unit. 
     
     
       8. The detection system as recited in  claim 1 , wherein the patient feature extraction unit are provided with means to perform calculations that make use of both data from the hotspot feature extraction unit and of the outputs of the first artificial neural network unit. 
     
     
       9. The detection system as recited in  claim 1 , wherein the second artificial neural network unit is arranged to calculate the likelihood for the patient having one or more cancer metastases, and wherein the unit are fed with the features produced by the patient feature extraction unit. 
     
     
       10. A method automatically detecting bone cancer metastases from an isotope bone scan image set of a patient, the method comprising the following steps:
 identifyingaccessing, by a computerized image processing system, an isotope bone scan image set, wherein each image in the isotope bone scan image set comprises a plurality of pixels with a value of each pixel corresponding to an intensity; 
 automatically segmenting, by the computerized image processing system, each image in the isotope bone scan image set to identify one or more anatomical structures of the a skeleton pictured in the set of each image in the isotope bone scan images image set, thereby forming an annotated set of images; 
 automatically detecting areas, by the computerized image processing system, a set of hotspots comprising one or more hotspots, each hotspot corresponding to an area of high intensity in the annotated set of images based on information regarding the anatomical structures corresponding to different portions of the skeleton of the images, said detecting of the set of hotspots comprising iteratively:
 identifying one or more skeletal image elements, each of the skeletal image elements corresponding to a region of an image in the annotated image set, wherein the region is associated with one of the anatomical structures;   detecting one or more hotspots in the annotated set of images, each hotspot corresponding a region of high intensity relative to its surroundings;   for each of the skeletal image elements, determining whether each of the skeletal image element comprises a detected hotspot;   calculating an average intensity of the skeletal image elements determined not to comprise a detected hotspot;   calculating a normalization factor, wherein a product of the normalization factor and the average intensity is a pre-defined intensity level; and   multiplying the value of each pixel in the annotated set of images by the normalization factor;   
 for each hotspot in the set of hotspots, extracting, by the computerized image processing system, a set of hotspot features for each hot spot detectedassociated with the hotspot; and  
 feeding, to a first artificial neural network unit arranged to calculatefor each hotspot in the set of hotspots, calculating, by the computerized image processing system, a first likelihood value corresponding to a likelihood for each hot spot of the hotspot set being a metastasis, based on the set of hotspot features extracted; 
 extracting a set of patient features based on the hotspots detected and on the likelihood outputs from the first artificial neural network unit; and 
 feeding, to a second artificial neural network unit arranged to calculate a likelihood that the patient has one or more cancer metastases, the set of patient features extracted associated with the hotspot. 
 
     
     
       11. The method of  claim 10 , wherein the step of processing extracted information further involves feeding, to the pretrained artificial neural network, for each hotspot in the set of hotspots, the set of hotspot features comprises at least one feature selected from the group consisting of the following:
 a value describing the eccentricity of each hotspot; 
 a value describing the skeletal volume occupied by an extracted hotspot region; 
 a value describing the maximum intensity calculated from all hotspots on the corresponding normalized image; 
 a value describing the hotspot localization relative to a corresponding skeletal region; 
 a value describing distance asymmetry which is only calculated for skeletal regions with a natural corresponding contralateral skeletal region(s); and  
 a number of hotspots in one or more certain anatomical region(s). 
 
     
     
       12. The method of  claim 10 , wherein the step of extracting information involves the further steps of:
 identifying a number of anatomical structures in the bone scan image(s); detecting a set of hotspots comprising one or more hotspots comprises:  
 detecting hotspots in each anatomical region by comparing the value of each pixel with a threshold value, different for each anatomical region; and  
 decidedeciding, for each hotspot, which anatomical region it belongs to. 
 
     
     
       13. The method of  claim 12  further comprising the step of:
 for each hotspot: determining the number of pixels having an intensity above a predetermined threshold level. 
 
     
     
       14. The method of claim  12  wherein the step of identifying a number of anatomical structures in the 10, comprising segmenting each image in the isotope bone scan image(s) further includes the step of segmenting the bone scan image(s) image set by a segmentation-by-registration method. 
     
     
       15. The method of  claim 14  wherein the segmentation-by-registration method comprises the following steps:
 comparing a each image of the bone scan image set with an a corresponding atlas image of an atlas image set, the each atlas image having anatomical regions marked; and 
 for each image of the bone scan image set, adjusting a copy of the corresponding atlas image set to the bone scan image set, such that anatomical regions of the atlas image can be superimposed on the bone scan image of the bone scan image set. 
 
     
     
       16. The method of  claim 10 , wherein the step of processing extracted information further involves feeding, to the pretrained artificial neural network, for each hotspot in the set of hotspots, the set of hotspot features comprises at least:
 a value describing distance asymmetry which is only calculated for skeletal regions with a natural corresponding contralateral skeletal region. 
 
     
     
       17. The method of claim 10, comprising calculating, for each hotspot in the set of hotspots, the first likelihood value using a pre-trained machine learning technique. 
     
     
       18. The method of claim 17, wherein the pre-trained machine learning technique is an artificial neural network (ANN). 
     
     
       19. The method of claim 10, wherein, for each hotspot in the set of hotspots, the first likelihood value corresponds to an output of a machine learning module that implements a pre-trained machine learning technique, and the output of the machine learning module is based at least in part on one or more of the hotspot features associated with the hotspot. 
     
     
       20. The method of claim 10, wherein for each hotspot in the set of hotspots, calculating the first likelihood value corresponding to a likelihood of the hotspot being a metastasis, based on the set of hotspot features associated with the hotspot comprises:
 determining from the one or more identified anatomical structures, an anatomical structure to which the hotspot belongs based on a location of the hotspot,   selecting one of a set of artificial neural networks (ANNs), wherein each ANN in the set of ANNs is associated with a specific identified anatomical structure of the one or more identified anatomical structures, and   calculating the first likelihood value using the selected ANN, wherein the specific identified anatomical structure with which the selected ANN is associated is the anatomical structure to which the hotspot belongs.   
     
     
       21. The method of claim 10, comprising calculating, by the computerized image processing system, a second likelihood value corresponding to an overall likelihood that the patient has one or more metastases based on the calculated first likelihood values. 
     
     
       22. The method of claim 21, comprising:
 for each hotspot in the set of hotspots, calculating the first likelihood value using a first artificial neural network (ANN), wherein:
 the first likelihood value corresponds to an output of the first ANN, and 
 the output of the first ANN is based at least in part one or more hotspot features in the set of hotspot features associated with the hotspot; and 
   calculating the second likelihood value based on an output of a second ANN, wherein:
 the output of the second ANN is based at least in part on one or more patient features, and 
 each of the one or more patient features is based at least in part on one or more of the first likelihood values calculated for each hotspot in the set of hotspots.