WO2021250710A1 - Procédé de calcul d'un indice de gravité dans des fractures traumatiques et système correspondant mettant en œuvre le procédé - Google Patents

Procédé de calcul d'un indice de gravité dans des fractures traumatiques et système correspondant mettant en œuvre le procédé Download PDF

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WO2021250710A1
WO2021250710A1 PCT/IT2021/050173 IT2021050173W WO2021250710A1 WO 2021250710 A1 WO2021250710 A1 WO 2021250710A1 IT 2021050173 W IT2021050173 W IT 2021050173W WO 2021250710 A1 WO2021250710 A1 WO 2021250710A1
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humerus
fragments
value
label
calculating
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PCT/IT2021/050173
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Fabrizio Fiorentino
Livia Renata Pietroluongo
Raffaele Russo
Daniel Riccio
Silvia Rossi
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E-Lisa S.R.L.
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Publication of WO2021250710A1 publication Critical patent/WO2021250710A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4504—Bones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/10—Computer-aided planning, simulation or modelling of surgical operations
    • A61B2034/101—Computer-aided simulation of surgical operations
    • A61B2034/105—Modelling of the patient, e.g. for ligaments or bones
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4504—Bones
    • A61B5/4509—Bone density determination
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

Definitions

  • the present invention relates to a method for calculating a severity index in traumatic fractures, in particular in the proximal third of the humerus of an individual.
  • the present invention concerns also a system for the implementation of this method.
  • Field of the invention
  • the invention relates to a method of the aforementioned type, studied and implemented in particular to define, by means of data from medical imaging instruments processed by a computer, a severity index of a proximal humerus fracture useful for classifying, in a precise manner, the fracture itself, according to an automatic multifactorial analysis of the parameters that characterize the traumatic event.
  • the present invention allows supporting the diagnostic phase of traumatic fractures of the proximal third of the humerus of a person, having available in an automatic way all the information necessary to carry out an appropriate therapeutic choice for the benefit of the person involved in a trauma.
  • the complexity resides in the low ability to properly figure out either by a radiographic point of view by means of a CT - Computer Tomography - the multiplicity of factors and parameters characterizing a specific traumatic event, such as, for example, the precise location of fragmented parts, their recognition or their specific interest.
  • the literature also reports another type of classification obtained through the CT evaluation of the humeral calcar and in particular for 4-part fractures, in which it is demonstrated how the evaluation of the type of this study can help the orthopedist to improve the anatomopathological understanding of the displacements of the fragments in relation to the forces that determined them.
  • an object of the present invention to overcome the aforementioned disadvantages by providing a method for calculating a severity index in traumatic fractures of the proximal third of the humerus of a specific individual, and a relative implementation system of the method.
  • Another object of the invention is to provide a method that allows analyzing automatically the severity of traumatic fractures of the proximal third of the humerus, providing all the information necessary to carry out a therapeutic choice as objectively as possible.
  • a further object of the present invention is to provide the tools necessary for carrying out the method and the apparatuses that carry out this method.
  • each section image S k is segmented in a significative region, comprising at least one bone structure, wherein at each pixel p is assigned a value 1, and in a non-significative region, wherein at each pixel p is assigned value 0, so that each section image S k is a binary image; C.
  • the step F of the method may comprise, after said sub step FI, a sub step F2 for calculating said severity index with respect to a volume of said humerus, wherein said volume of said humerus is the volume comprised between a plane ⁇ , arranged in correspondence of the anatomical neck of said humerus, and a plane ⁇ , substantially parallel to said plane ⁇ , and arranged in correspondence of the surgical neck of said humerus, and wherein said volume of said humerus is subdivided, by means of a plane ⁇ , into a medial zone and a lateral zone which is opposite to said medial zone, said severity index being calculated by means of the following formula:
  • the severity index of said medial zone IS M is calculated by means of the following formula: where p M is a value associated with the respective fracture configuration of said medial zone with respect to said plane ⁇ and/or said plane ⁇ , f( ⁇ C M ) is a value associated with the comminution of the respective fracture configuration of said medial zone and f(B LM ) is a value associated with the bone loss of said medial zone.
  • the severity index of said lateral zone IS L is calculated by means of the following formula:
  • IS L p L + f( ⁇ C L ) + f(B LL )
  • p L is a value associated with the respective fracture configuration of said lateral zone with respect to said plane ⁇ and/or said plane b
  • f( ⁇ C L ) is a value associated with the comminution of the respective fracture configuration of said lateral zone
  • f(B LL ) is a value associated with the bone loss of said lateral zone.
  • said step A comprises the following sub steps: A1. given V the set of pixels p contained in the entire volume of said section images S k , rescaling the values in V in the range [0, 4096] by means of a min-max transformation and calculating the average mV, the standard deviation sV and the value gmax,V of the grey tone of each pixel p, such that gmax, V > mV; A2.given, for each section image S k , the previous section image S k-1 and the subsequent section image S k+lr calculating, for each pixel p in the section image S k , a weight w as the geometrical average of the grey tone values that said pixel p assumes in said section images S k-lr S k e S k+lr rescaling said weights w in the range [0,1] and replacing the grey tone of pixel p with the value (l+ w)-p; A3, for each section image S
  • the step B may comprise the following sub steps: B1. given a section image S k , rescaling the grey tones of the pixels p comprised in said section image S k in a range [0, Gmax] ; B2. given a threshold value t h , calculating the foreground entropy E f and the background entropy E b , according to the following formulas: where H(gi) is the total number of times that the grey tone g t appears in the image section S k , and P(g t) is the probability that the grey tone gi appears in the image section S k; B3.
  • the step C of the method may comprise the following sub steps: Cl. detecting the connected components of each image section S k ; C2. extracting a plurality of morphological features for each connected component detected in said sub step Cl; and C3.
  • each connected component to a respective class and labelling each connected component with a respective label, so that said humerus is labelled with a first label, said shoulder blade is labelled with a second label which is different from said first label, and a further bone region different from said humerus and said shoulder blade is labelled with a third label which is different from said first label and said second label.
  • the plurality of morphological features extracted in said substep C2 comprises the following morphological features: a first real value e kj equal to the ratio of the distance between the foci and the major axis of the ellipse that approximates said respective connected component; a second real value s kj equal to the ratio between the area of the respective connected component and the area of the convex envelope containing said respective connected component; a third real value a kj equal to one minus the ratio between the minor axis and the major axis of the ellipse approximating said respective connected component; a fourth real value A kj equal to the area of the respective connected component; and a fifth real value r kj equal to min(l- where 8 represents the mean square error with which an ellipse approximates the perimeter of the respective connected component.
  • each connected component is labelled, in said substep C3, as said shoulder blade if , as said humerus if or said further bone region, in the remaining cases.
  • the step D of the method may comprise the following sub steps: D1. calculating a bone density weight w for each pixel labelled as humerus, by means of said first label, in each section image S k; D2. labelling said at least one connected component; D3. calculating a compatibility coefficient between the labels of said sub step D2; and D4. applying a region-growing algorithm to detect fragments.
  • the step E of the method may comprise the following sub steps: E1. extracting a set of features for each fragment; E2. recomposing said plurality of fragments by means of said reference model; E3. coupling fragments that are compatible with a fracture rhyme on the basis of a similar rigid transformation; and E4. reassembling said plurality of fragments according to said sub steps E3 and E4.
  • It is also object of the present invention a computer-readable storage medium comprising instructions which, when executed by a computer, cause the processor to perform the method steps A-F.
  • figure 1 shows a flowchart of an embodiment of a method for calculating a severity index in traumatic fractures of the proximal third of the humerus of one individual, according to the present invention
  • figure 2a shows a side view of a control volume of the humerus
  • figure 2b shows a side view of the control volume of figure 2a defined by means of a ⁇ and a ⁇ plane
  • figure 3a shows, in a perspective view, a subdivision, by means of a ⁇ plane, of the control volume of figure 2, in a anterior and a posterior zone
  • figure 3b shows, in a perspective view, a subdivision, by means of a plane ⁇ , of the control volume of figure 2, in a lateral region and a medial region
  • figure 4 shows, in a front perspective view, a subdivision, by means of said plane d and
  • the method for calculating a severity index in traumatic fractures of the proximal third of the humerus of an individual comprises the following steps:
  • each of the steps A. - F. mentioned above is performed fully automatically, for example, with the aid of a computer aided system - Computer-Aided System or CAS.
  • each of the steps A. - F. may provide the possibility of a correction manual intervention by an operator, through a suitable interface.
  • Step A pre-processing of a volume of images produced by a CT exam
  • CT imaging equipment exhibits high variability in terms of image quality.
  • this requires the need to standardize the features of the images, by means of said step of pre-processing of a plurality or a volume of images previously acquired by the CT technique.
  • the input data of said step A comprise a volume of CT images, namely images of sections - or slices - of the object under examination obtained by Computed Tomography applied to an individual.
  • such images are CT images.
  • images obtained by various diagnostic imaging techniques This step A. allows correcting the levels, or gray tones of each slice of the volume of the input CT images, in order to reduce the variability of the levels or gray shades both between one slice and another and within of the same slice.
  • the output data of step A include, however, a normalized volume of images CT, namely images that disregards not as much as possible by the acquisition conditions, i.e., that have features as objective as possible.
  • Step A comprises the following sub-steps:
  • the values in V are in the range [0, 2 12 ], in accordance with the depth of pixels (12 bits) provided by the CT devices acquisition.
  • the values in V are rescaled in the range [0, 4096] by means of a known min-max transformation and on V are calculated the following parameters: the average mV, the standard deviation sV and the value of the gray tone gmax,V, such that gmax,V > mV, with the maximum number of occurrences in V, where occurrence means the number of times a value is present in V ;
  • the gray tones in the slice S k are rescaled to the interval [0, 4096], by means of a min-max transformation .
  • Step B segmentation of the volume of the pre- processed images .
  • the Hounsfield (HU) scale also called the CT number, is a unit scale adopted to quantitatively describe radiodensity.
  • the known solutions for the segmentation and extraction of bone structures from a volume of CT images allow mapping the gray tones of the pixels in a slice in the corresponding HU index, by means of a linear transformation, and to select as pixels of interest those whose value is contained in the interval [300, 900], generally referred to as the range of variation of bone density.
  • the method according to the present invention provides for a local segmentation step of the volume of images leaving the pre-processing step A., in which the optimal threshold for the segmentation of the bone structures in each image is automatically determined.
  • the input data of segmentation step B comprise the volume of normalized CT images, i.e., the output data of step A.
  • the segmentation step B allows partitioning each slice in significant regions, resulting in an automatic way the optimal threshold for segmenting or dividing, the pixels corresponding to the bone structures from the additional pixels of the image.
  • a threshold such that all pixels p with gray tone greater than threshold th k are set to 1, and considered pixels of foreground, namely bone, while the pixels with a gray tone lower than the threshold th k are set to 0, and therefore considered as background pixels, that is, of irrelevant content.
  • the segmentation step produces a binary mask, where only the pixels corresponding to the bone structures in S k are set to 1.
  • the concept underpinning the segmentation process is that of automatically find that value th k for the slice S k , which divides the pixels into two sets, i.e., foreground and background, for which it results that the corresponding internal entropy is maximum.
  • the output data of the step B comprise a volume of segmented or binary CT images, i.e., images having only two possible values for each pixel, 0 for non-relevant content or background, and 1 for the relevant or foreground content.
  • Step B includes the following sub-steps:
  • the gray tones are rescaled in an interval [0, Gmax] , in order to improve the efficiency and effectiveness of the algorithm.
  • Gmax is set to 512;
  • H(g i ) is defined as the total number of times the gray g i tone appears in the slice S k .
  • P(g i ) is also defined as the probability that the gray tone g ⁇ appears in the slice S k , that is as H(g i ) divided by the total number of pixels within the slice s k .
  • the entropy of the foreground and the entropy of the background are calculated, according to the following formulas:
  • the entropy of the foreground and the background are combined in a single quantity the difference between the adjacent values E tot is defined as the value is calculated and is defined. Again, the difference between adjacent values of is defined; and the threshold th k for segmentation of the slice S k is determined as the value
  • FIG. 6 it is seen a slice I relative to the humeral head of an individual and a slice II after the segmentation step described above, and a graph of the corresponding quantities Ey, E b and E tot , and the variation of th in [0, Gmax] .
  • Step C identification of humerus and scapula
  • the bone structure extracted from a volume of CT images can include several elements including, for example, humerus, clavicle, scapula, ribs, thoracic vertebrae, and or sternum.
  • the present method comprises a step for the detection of the scapula and humerus, so as to be able to exclude by subsequent steps all the elements of the previously segmented bone structure, which are not of interest.
  • the input data of the step C comprise the volume of segmented CT images, namely the output data of the step
  • the scapula and humerus identification step allows identifying these bone elements within each image of the segmented CT image volume.
  • the output data of the step C comprise the volume of the CT images, wherein in each image the humerus and the scapula have been identified.
  • Step C comprises the following sub-steps:
  • the CT volume is processed slice by slice in the axial direction, from top to bottom.
  • an algorithm for labeling the 8-connected components is applied to each slice S k .
  • This known algorithm produces as a result a matrix of the same size as S k , in which all the pixels belonging to the same connected component c kj are marked by the same label value l kj .
  • the system maintains a different set of labels L, which is global with respect to the CT volume and which initially contains all and only the labels present in the slice S 1 .
  • the algorithm proceeds slice by slice, trying to assign the labels already present in L to the objects under examination in the current slice, on the basis of a compatibility criterion.
  • the procedure for assigning labels on the current slice S k is as follows.
  • ⁇ k-1 contains multiple labels, the one with the most overlap with c kj is assigned to c kj .
  • L represents the set of labels assigned to the different objects in the bone structure extracted during the segmentation step.
  • this type of labeling has the sole purpose of identifying the scapula and the humerus.
  • such labeling cannot be considered as the labeling of the individual fragments produced by fracture of the humeral head, because if several fragments adjacent to one another have points of contact, such fragments are necessarily labeled with the same label.
  • Area A kj that is a real value equal to the area of the connected component; and e.
  • Regularity r kj i.e. a real value equal to min(l- where 8 represents the error root mean square with which an ellipse approximates the perimeter of the connected component.
  • sub-step C3. the connected components of all slices in the CT volume are examined individually.
  • each connected component is assigned to the class: a. Scapula, if b. humerus, if r kj >0.4; and c. other, in the remaining cases.
  • the displaced fracture of the humerus head produces fragments, which, due to their morphological features, may not have been assigned to the humerus class.
  • the method according to the present invention being known the typical arrangement of such fragments near the surgical neck, identifies an approximation of the latter as a reference point and assigns to the humerus class all those fragments, not classified as scapula, whose distance from it is less than a threshold dth.
  • the algorithm for the search of the approximation of the surgical neck operates only on the connected components c kj having as a label in L the one assigned to the humerus class in the previous step.
  • H The values in H are, then, rescaled in the interval [0,1] by means of a min-max transformation; b) indicated with href the value with the highest number of occurrences in H, a new histogram H' is constructed, such that H(k)'represents the number of c kj in the previous slices (S1,..., Sk-1), for which it is true that c) the slice S kcg which approximates the surgical neck is identified as
  • the threshold dth is calculated as the diameter of that component.
  • All the fragments, in the CT volume not labeled as scapula and having at least one voxel, i.e., a volumetric pixel, at a distance from c kcgj less than dth, are labeled as the humerus.
  • Step D identification of the fragments of the humerus head .
  • the fracture of the humeral head produces fragments which number, size, and dislocation constitute an essential information for the calculation of the severity index.
  • the step D of the method proposed solves this problem by means of labeling of the connecting components and the first slice S k and propagation of such labels along the axial direction of the CT volume or creation of new labels on the basis of a superposition criterion of the same connected components.
  • a compatibility coefficient is then associated with the generated labels, while a weight proportional to bone density is associated with the individual pixels.
  • a region growing algorithm aggregates the different pixels of the volume into different sets, corresponding to the fragments, on the basis of the previously calculated information.
  • the input data of said step D comprise the volume of the CT images, wherein in each image humerus and the scapula have been identified, namely the output data of the step C.
  • step D allows identifying the fragments of the humeral head generated by a fracture, even if such fragments are not perfectly disjoint but have one or more point contact between them.
  • step D a real model 0 of the fully reconstructed humerus is generated (since it is fragmented into several parts).
  • the output data of said step D comprise the 3D fragments of the humerus head identified in the previously labeled CT volume.
  • the step D comprises the following sub-steps:
  • step D1 weighted region growing algorithm application for fragment labeling.
  • step D1 a weight proportional to its bone density is assigned to each pixel.
  • the CT volume is processed slice by slice in the axial direction from the top to the bottom.
  • This known algorithm produces as a result of a matrix of the same size as S k , in which all the pixels belonging to the same connected component c kj are marked by the same label value l kj .
  • the system maintains a different set L of labels, which is global with respect to the CT volume and which initially contains all and only the labels present in the slice S- L .
  • the algorithm proceeds slice by slice trying to assign the labels already present in L to the objects under examination in the current slice, on the basis of a compatibility criterion.
  • the procedure for assigning labels on the current slice S k is as follows.
  • ⁇ k-1 contains two or more labels
  • the intersection region between R kj and c kj and the set of pixels in S k-1 with label lj is determined.
  • This index is a fundamental part of the region growing algorithm for identifying the fragments.
  • the compatibility index indicates the probability that pixels labeled with two different labels can be merged within the same fragment.
  • C and D two square matrices of dimensions
  • the algorithm proceeds in three steps: a) the CT volume is processed slice by slice in the axial direction from top to bottom.
  • C(i,j) is set with the value where A k i , A k,j , and d respectively represent the area of the connected components labeled with iandjand d is the distance between the two components.
  • D(i,j) is set with the value 1/d. Also, for each pair of labels i in S k and j in S k+1 , it is set with the value 1/d. b) the CT volume is processed slice by slice in the sagittal direction.
  • the CT volume is processed slice by slice in a coronal direction.
  • step D4 it is applied a region growing algorithm to locate 3D fragments in the TC labeled volume.
  • V LF r The points of the volume already labeled are indicated with V LF r and the points of the volume not yet labeled are indicated with V NLF .
  • the algorithm operates by iterating two steps: a) selection of the starting seed; and b) expansion of the label.
  • step a at each iteration k the algorithm chooses a seed from which starting, that is a label l k L, not yet selected in the previous iterations, from which starting the expansion process.
  • the seed l k is selected as that label in L, for which the sum of the compatibility indexes with all the other labels is maximum. That is, like that label that has the greatest probability of expansion.
  • th j is calculated equal to the average of the compatibility indexes in C of l k with all the other labels.
  • step b the label expansion algorithm exploits the information generated in steps D1 and D4,a, i.e., the weights wp, the threshold th j and the set of points P lk .
  • Step E recomposition and alignment of the fragments of the humeral head with respect to the reference model M.
  • control volume of the humerus is the volume between the lid and the diaphysis and, more precisely, between the ⁇ and ⁇ planes.
  • this control volume comprises the tuberosity and the calcar.
  • Step E solves the problem in the recomposition of a set of fragments and their alignment with respect to the reference model M, in a concomitant way.
  • the step E input data comprises the 3D humeral head fragments found in the CT volume, i.e., the step D output data.
  • step E two types of regions are considered for the single fragment, namely the intact zones, and the fracture zones.
  • a set of characteristic points is identified, used, then, for a dual matching process, both at the local level, between pairs of fragments, and globally, between more fragments groups, with the objective of maximizing the consistency of the re-composition with respect to the reference model M.
  • step E is employed to align the real model 0 with the reference model M.
  • the output data of step E comprise the different fragments of the humeral head recomposed according to the reference model M and aligned with it with respect to the scale, the position, and the orientation in the space.
  • Step E comprises four sub-steps:
  • sub-step E1 given a fragment F i , a set of reference points, considered as characteristic points, is identified within this fragment.
  • the algorithm applied for the determination of the characteristic points is based on the ISS - Intrinsic Shape Signatures descriptor.
  • the known ISS algorithm considers for each 3D point its support region, of which it calculates the covariance matrix and marks it as a reference point if the difference in magnitude between the first two most significant eigenvalues is maximum. For each of the reference points P j , a features vector V Pi is then calculated, which describes its local geometric properties.
  • each fragment is placed in correspondence with the region most similar to it in the reference model M, through a process of matching local between the features of the fragment and those of the reference model M.
  • the set of corresponding points thus identified is then reduced.
  • the Euclidean distance is measured, and if the corresponding pair of points identified on the reference model M is at a distance greater than a predetermined threshold, the pair is eliminated from the set of potential matches.
  • the algorithm for the matching of fractures between a generic pair of fragments f i and f j has the purpose of coupling fragments that are compatible with respect to the fracture gap according to a rigid affine transformation T ij .
  • the algorithm characterizes the rime of fracture, namely the exact interruption point of the bone, of each fragment f on the basis of sets of characteristic curves C i . Given two fragments andf and f j, with their respective sets C i and C j , the algorithm solves a Largest Common Point-Set (LCP) problem, in order to find the transformation F ij , which minimizes the distance between f i and f j . If the distance between the fragments, with respect to the relative fracture lines, is less than a predetermined threshold, they are considered compatible with the transformation found.
  • LCP Largest Common Point-Set
  • sub-step E4 the method carries out the global reassembly of the fragments, taking into account both the matching with the reference model M, and the compatibility between the different pairs of fragments.
  • the algorithm represents the fragments as a graph, in which a node is represented by a pair (f i , T i ) r where fi is the i-th fragment, while T i is a transformation matrix that maps the corresponding fragment in its final position.
  • An arc characterized by a transformation T ij is inserted between two nodes of the graph if the two fragments are compatible, according to the criterion described in the previous step.
  • a global compatibility index between two fragments is defined as the sum of two different components, that is, one relating to the compatibility of the fracture lines and one relating to the correspondence with the reference model
  • the algorithm solves a multi-parametric search problem of the subgraph of the graph previously constructed, and which maximizes the sum of the global compatibility indices between the nodes.
  • Phase F estimate of the severity index (IS) .
  • the recomposition process reassembles the different fragments extracted from the CT volume by means of steps A-D, in the 3D model of the humerus, or in the real model
  • part refers to a bony region of the humerus. This part is whole, namely, it is free of fragments, in the absence of a fracture, or it can include one or more fragments in the presence of a fracture.
  • the method is capable of automatically calculating the weights associated with the codes shown in the tables adopted for the classification.
  • the input data of step F includes the different fragments of the humeral head recomposed and aligned according to the reference model M, i.e., the output data of step E.
  • Step F allows calculating the severity index of a fracture of the proximal third of the humerus of a specific subject.
  • the data output of the step F includes one or more values of the severity index.
  • Step F includes two sub-steps FI and F2, which respectively have the objective of extracting all the measures necessary for estimating the severity index and calculating the latter on the basis of the formulas and the weight tables illustrated below in the description.
  • the annotations relating to the margins of the individual parts can be directly mapped from the reference model M (previously annotated) to the real model 0. Since each voxel in the real model 0 is associated with the label of one of the fragments identified in step D, in sub-step F1, the system is able to automatically calculate the following information:
  • the displacement of the lid (neutral, varus, valgus, posterior and anterior dislocation) - the inverse spatial transformation to that applied for the relocation of the lid and the original position of the latter with respect to that occupied after the realignment, and to the glenoid cavity, or to the joint cavity of the bone, allow the classification of the displacement.
  • Sub-step F2 evaluates the severity index. As will be better described in the following, it is calculated by assigning weights to different factors and parameters that characterize the fracture, such as the fractured segments in the medial or lateral zone of the control volume, or the position of the humeral head and its fragmentation (head split), or the comminution and the bone loss of the parts.
  • the segments considered for the formulation of the severity index are those enclosed by the control volume, that is the calcar in the medial zone, the greater tuberosity GT, and the lesser tuberosity PT in the lateral zone and the lid.
  • severity mechanics The concept of severity can be linked to three different aspects: complexity of the fracture in anatomical-pathological terms, or the link between the parts (anatomical-pathological severity), risk of necrosis of the humeral head (biological severity), and difficulty in surgical reconstruction (severity mechanics).
  • the overall value of the severity index is given by the weighted combination of the various factors or parameters that make up the index itself.
  • the weighted values of the various factors or parameters taken into consideration in the calculation are given below by way of example but not by way of limitation.
  • the severity index for fractures of the proximal third of the humerus can be considered as a single value or also as the sum of three separate components as reported in the following formula: IS — IS M + IS L + IS c where IS M is the medial severity index, IS L is the lateral severity index, IS C is the lid severity index . In the rest of the description, all the components of the index will be specified in detail.
  • the comminution of a bony part is related to the energy of trauma and it was used as a measure of the seriousness of the joint injury.
  • a greater energy input involves a greater number of fragments and also of a smaller size.
  • the energy absorbed by the bone during an impact is released when the bone breaks, so the more energy the bone can absorb and the greater the release in the event of a fracture, the greater the comminution.
  • the parameter of the comminution turns out to be very important for the purposes of assessing the severity of a fracture of the proximal third of the humerus.
  • the calcar occupies the medial area of the control volume. It is an important hinge point and the more intact its surface, the more stable the lid is, so it is relevant both from the mechanical and the vascularity point of view.
  • the plane d divides the medial zone formed by the calcar into two parts, which have a different role: the posterior calcar CP and the anterior calcar CA.
  • the posterior calcar CP is of greater relevance because it is located posteriorly and therefore closer to the posterior circumflex artery, so the circulation is greater.
  • the posterior calcar CP from a surgical point of view, is not very visible due to its position. Therefore, the surgical approach is more difficult because typically the surgical approach window is anterior or lateral.
  • the anterior calcar CA is of less relevance because it is more visible from a surgical point of view.
  • the anterior calcar CA is close to the anterior arcuate artery but being more visible its management is easier.
  • each configuration of the medial zone is that indicated in Table 2 (or links table of the medial zone of the control volume).
  • Figure 7 shows a graphic representation of the fracture conformations of the medial zone.
  • each medial conformation it is associated a second value ⁇ C M .
  • This second value ⁇ C M depends on the additional level of comminution that is automatically found in the case examined. In particular, reference is made to the difference ( ⁇ C M ) between the detected comminution C R and the medial reference comminution C MR if .
  • the value associated with the additional comminution of the medial zone is that indicated in Table 3 (or table of comminution additional of the medial zone of the control volume).
  • a third value B LM is added to the evaluation of the severity index of the medial zone.
  • This third value B LM depends on the medial bone loss.
  • medial zone bone loss is that indicated in Table 4 (or medial zone bone loss table of the control volume).
  • the lateral area of the control volume is occupied by the lesser tuberosity PT and the greater tuberosity GT. These two tubercles are the seat of the insertions of the rotator cuff muscles, thus playing an important role in the movement and stabilization of the glenohumeral joint.
  • the greater tuberosity GT is important because it is attached to the rotator cuff. If the fragment is large, the vascular crisis is less, as is the weight of the tendon damage of the periosteum.
  • the greater tuberosity GT has greater importance than the lesser tuberosity PT, but less than in the calcar and the lid.
  • the lesser tuberosity PT has less relevance because it is difficult for it to necrosis. However, the lesser tuberosity PT is still an important part as it is located near the subscapularis tendon that attaches to it. This makes it important for a possible reconstruction.
  • the value associated with each configuration of the lateral zone is that indicated in Table 5 (or table of the bonds of the lateral zone of the control volume).
  • Figure 8 shows a graphical representation of the conformations of the fracture in the lateral zone.
  • a second value is associated with each lateral configuration ⁇ C L . This second value depends on the additional level of comminution that is automatically found in the case examined.
  • a third value B LL is added to the evaluation of the severity index of the lateral zone IS L .
  • This third value B LL depends on the bone loss of the lateral zone (lateral bone loss).
  • the value associated with the bone loss of the lateral zone is that indicated in Table 7 (or the table of the bone loss of the lateral zone of the control volume).
  • the lid is the joint part where the movement takes place and is therefore the area that most easily undergoes necrosis.
  • the lid bone is a bone that is likely to easily de-vascularize.
  • the blood vessels that pass through the calcar and lead to the lid are unique and very important.
  • the lid has greater importance than all the other parts.
  • the value associated with each position of the lid is that indicated in Table 8 (or table of the lid positions).
  • Figure 9 shows a graphical representation of the conformations of the positions of a lid.
  • the front luxation is the anterior dislocation is less serious because it is more easily identified without any damage when the reduction is carried out.
  • a second value is associated with each position of the lid.
  • This second value influences the severity of the fracture of the lid and depends on the size of the largest fragment of the lid, which is automatically found in the case examined.
  • the fragment size is reported as a percentage of the total surface of the lid.
  • the value associated with the size of the largest fragment (HF - head fragment) of the lid is that indicated in Table 9 (or table of the severity of the head split).
  • This third value B LC depends on the number of fragments in which the head is fractured and on the bone loss of the lid.
  • the value associated with the number of fragments and the bone loss of the lid is that indicated in Table 10 (or table of the number of fragments and bone loss of the lateral zone of the control volume).
  • the severity index will be given by the following formula: The calculation of the severity index has required the consideration of various factors or parameters characterizing the fracture.
  • the present invention makes it possible to construct a real model of the humerus fracture and to compare this real model with a reference model of the intact bone structure of the humerus, in order to provide an optimal method of reconstruction of the humerus fracture by means of the calculation of indices of severity of such fracture.
  • the system S which implements the method for calculating a severity index in traumatic fractures, object of the present invention, essentially comprises a processing unit SI, display means S2, storage means S3, interface means S4 and power supply means S5.
  • the processing unit SI is the functional and main element of the system S, and, for this reason, it is connected and in communication with the other elements of the system S itself.
  • the processing unit SI is equipped with calculation and processing means, configured to run a software for calculating a severity index in traumatic fractures, as well as to interface with the other elements of the system S.
  • Said processing unit SI is also configured to control and coordinate the operation of the elements of the system S, with which it is connected and in communication .
  • Said processing unit SI can be constituted by a computer or a plurality of computers, possibly connected in the cloud.
  • said processing unit SI comprises a motherboard, a processor with a minimum frequency of 1,5 GHz, and a RAM memory having a minimum size of 4 Mb
  • said display means S2 comprises a monitor with a minimum resolution of 800x600
  • said storage means S3 comprises a hard-disk having a minimum size of 500Mb
  • the interface means S4 comprises a mouse and a keyboard.
  • the supply means S5 allows supplying said system S.
  • said system S can comprise further elements other than those mentioned above.
  • An advantage of the method according to the present invention is that of automatically analyzing the severity of traumatic fractures of the proximal third of the humerus, providing all the information necessary to carry out an appropriate therapeutic choice.
  • a further advantage of the method according to the present invention is that of constructing a real model of the fracture of the humerus and comparing this real model with a reference model of the intact bone structure of the humerus, to provide an optimal way of reconstructing the fracture by means of a calculation, fracture severity indexes.

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Abstract

La présente invention concerne un procédé de calcul d'un indice de gravité dans des fractures de l'humérus d'un individu, à partir d'une pluralité d'images de section Sk dudit humérus, où k = 1...N, N étant un nombre entier positif, capturé au moyen d'une technique d'imagerie diagnostique, caractérisé en ce qu'il comprend les étapes suivantes consistant : A. à normaliser les niveaux de gris de chaque image de section Sk de ladite pluralité d'images Sk, de telle sorte que chaque image de section Sk ait le même niveau de gris ; B. à partir desdites images normalisées dans ladite étape A, à segmenter chaque image de section Sk, dans une région significative, comprenant au moins une structure osseuse, dans laquelle il est attribué une valeur 1 à chaque pixel p, et dans une région non significative, dans laquelle il est attribué une valeur 0 à chaque pixel p, de telle sorte que chaque image de section Sk soit une image binaire ; C. à partir desdites images de section Sk segmentées dans ladite étape B, à identifier au moins un composant connecté correspondant audit humérus, ou à un omoplate, dans ladite structure osseuse comprise dans chaque image de section segmentée Sk, et à étiqueter ledit composant connecté avec une étiquette respective, de telle sorte que ledit humérus soit marqué d'une première étiquette et que ledit omoplate soit marqué d'une seconde étiquette qui est différente de ladite première étiquette ; D. à identifier une pluralité de fragments de la tête dudit humérus, à étiqueter chaque fragment de ladite pluralité de fragments avec une étiquette respective et à générer un modèle réel (0) dudit humérus à partir de ladite pluralité de fragments ; E. étant donné un modèle de référence (M) de la structure osseuse intégrale dudit humérus, à recomposer et à aligner ladite pluralité de fragments, identifiée dans ladite étape D., dans ledit modèle réel (0) selon ledit modèle de référence (M) ; et F. à calculer un indice de gravité de ladite fracture dudit humérus à partir de ladite pluralité de fragments recomposée et alignée dans ladite étape E. La présente invention concerne également un système qui met en œuvre ledit procédé.
PCT/IT2021/050173 2020-06-09 2021-06-08 Procédé de calcul d'un indice de gravité dans des fractures traumatiques et système correspondant mettant en œuvre le procédé WO2021250710A1 (fr)

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CN116993764B (zh) * 2023-09-26 2023-12-08 江南大学附属医院 一种胃部ct智能分割提取方法

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