Development of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies. Christian Salvatore

Transcription

1 Development of a Decision Support System for the automatic diagnosis of medical images from brain MRI studies Christian Salvatore Dottorato di Ricerca in Fisica, Scuola di Dottorato di Scienze Università degli Studi di Milano - Bicocca - XXVIII ciclo - Supervisor: Prof. Marco Paganoni External supervisor: Dott.ssa Isabella Castiglioni (IBFM-CNR; HSR) September, 28 th 2015

2 Overview Introduction Decision Support Systems Magnetic Resonance Imaging Machine Learning > Support Vector Machine The Machine Learning method Image preprocessing Feature extraction and selection Classification Computation of activation patterns for biomarkers extraction Application I > Parkinson s Disease Application II > Alzheimer s Disease Application III > Eating Disorders Conclusions

3 Introduction

4 Introduction > Decision Support Systems in medicine Role of Physics in Medicine R&D of medical devices (diagnosis, therapy ) Definition of protocols for the acquisition of medical images Implementation and development of algorithms for the analysis and processing of medical images Decision Support Systems in Clinical Medicine Assisted Diagnosis Objective clinical assessments Increasing in Diagnostic Accuracy Early and Differential Diagnosis

5 Introduction > Magnetic Resonance Imaging Nuclear imaging technique which produces 3D structural images Vertical magnetic field Precession motion of nuclei Magnetic momentum Radiofrequency pulses aimed to rotate the global magnetization (Larmor frequency for Hydrogen at 1.5T = 63.9MhZ) Spin-spin and spin-lattice interactions -> relaxation of magnetization vectors

6 Introduction > Magnetic Resonance Imaging T1 weight Cerebrospinal fluid (CSF) White and grey matter Fat

7 Introduction > Image Processing in Medicine Univariate analysis MultiVariate Pattern Analysis (MVPA) Correlation-based one-nearest neighbor classifier Neural network classifier Linear Discriminant Analysis Support Vector Machine (SVM) MACHINE LEARNING PREDICTIVE MODELS

8 Introduction > MVPA from Machine Learning Machine learning predictive models to predict a variable of interest (e.g. mental state 1 vs. mental state 2, or patients vs controls) from a voxel-based pattern (activation/anatomy)

9 Introduction > MVPA from Machine Learning The variable to be predicted when the variable comprises a set of discrete classes, the problem is referred to as Classification Variable = LABEL

10 Aim why SVM in brain imaging studies?

11 The machine learning method

12 The Machine Learning method > Schematic flowchart

13 The Machine Learning method > Image preprocessing MRI images Spatial Normalization Standard Template

14 The Machine Learning method > Feature extraction Principal Components Analysis (PCA) Extraction of the most significant features from a dataset X of (possibly) correlated variables : 1) application of an orthogonal transformation to the dataset X, which results in a set of orthogonal (uncorrelated) variables called principal components (eigenvectors) of the original dataset principal components = eigenvectors of C X X T 1) The space spanned by principal components is called PCA-space 2) PCA coefficients or scores are obtained by projection of the data onto the PCA-space PCA provides a measure of the variance explained by each component

15 The Machine Learning method > Feature selection Fisher s Discriminant Ratio (FDR) FDR can be obtained for each component applying the following formula to the PCA coefficients FDR k and 2 represent the two classes involved FDR provides a measure of the class discriminatory power of each component Feature selection is made by ranking PCA coefficients according to the FDR criterion (higher FDR = higher discriminatory power) Ranked PCA coefficients and associated labels can then be used for classification

16 The Machine Learning method > Classification Support Vector Machine (SVM) A set of known data (the training set) is used to estimate (to learn) the parameters of a model relating particular data attributes (the features ) to variables (the labels ) Once the parameters are learned (trained SVM), the model is applied to predict the label of new data (the testing set) AD subjects TRAINING SET Controls new subject Trained SVM Predicted label

17 The Machine Learning method > Classification Support Vector Machine (SVM) Image space N subj Feature space Data (Features, N sbj) 2 1 w d d d = distance from the hyper-plane 1 TRAINING SET New sbj pl x Predicted label = pl(x) N x w l k x, x i 1 i i i b l 1,2 TESTING SET

18 The Machine Learning method > Classification Support Vector Machine (SVM) pl N x w l k x, x i 1 i i i b Predictive model or decision function 1 Minimization of the empirical classification error Maximization of the separation distance between the two training classes (that is why the decision function is often called maximum margin hyper-plane) 2

19 Introduction > Classification Support Vector Machine (SVM) 1. Health Subject 1 2. Health Subject 2 3. Health Subject 3 Group CONDITIONS M. Health Subject M 1. Pathological 1 2. Pathological 2?? Single-subject classification Test +1 class 3. Pathological 3 M. Pathological M

20 The Machine Learning method > Weight maps Image space Feature space N subj 2 pl Data (Features, N sbj) N x w l k x, x i 1 i i i b 1 d 1 w d d = distance from the hyper-plane TRAINING SET Image maps of the most important voxels for the classification of 1 vs. 2 N sbj i=1 i x w = i Weight Maps Back projected image (eigenbrain)

21 The Machine Learning method > Weight maps Issues Predicted label = pl(x) pl N x w l k x, x i 1 x l 1,2 i i i b The combination of all weights defines the model Weights at each voxel are dependent on one another No voxel-wise statistical tests can be performed

22 The Machine Learning method > Activation patterns Image space Feature space N subj 2 y Data (Features, N sbj) N x w t k x, x i 1 i i i b 1 d 1 w d d = distance from the hyper-plane TRAINING SET Activation Maps w f N sbj = i=1 Features f,i x w i Image maps of voxel-based pattern distribution of brain structural differences between 1 and 2 COV [Data(Features, N sbj)] x w f = transformed weighted features

23 Application I Parkinson s Disease

24 Application I > Parkinson s Disease Parkinson s Disease (PD) Diagnosis Critical issue: to achieve an individual differential diagnosis Poor accuracy, specificity and sensitivity at visual inspection PD is difficult to be differentiated from other parkinsonian conditions such as Progressive Supranuclear Palsy (PSP) To date, the only validated MRI-based measurement employed in clinical practice of PD derives from conventional MRI using manual morphometric quantification

25 Application I > Parkinson s Disease Cohort 28 Healthy Control (HC) subjects 56 patients 28 Parkinson s Disease (PD) 28 Progressive Supranuclear Palsy (PSP) Healthy control subjects of similar age as both patients groups Patients recruited and data acquired at the Institute of Neurology, University Magna Graecia, Catanzaro MR images MRI system (1.5 T) Signa NV/I GE Medical Systems, USA FOV = 24cm Slice thickness = 1.2mm TR = 15.2ms; TE = 6.7ms T1-weighted 3D dataset

26 Application I > Parkinson s Disease Performance evaluation 1 Leave-One-Out (LOO) Cross Validation: N-1 subjects for training The remaining (1) subject for testing Multiple (N) rounds 2 N/2 Validation: 50% randomly chosen subjects for training The remaining 50% subjects for testing

27 Application I > Parkinson s Disease Comparisons PSP vs. PD PSP vs. CN PD vs. CN

28 Application I > Parkinson s Disease Image pre-processing Axial view of a MR scan of a clinicaly diagnosed PD patient before (a) and after (b) normalization to the MNI space

29 Application I > Parkinson s Disease Feature extraction + selection PSP (28) vs. PD (28): PCA coefficients (1 st, 2 nd and 3 rd components that showed highest FDR)

30 Application I > Parkinson s Disease Classification (training) PSP (28) vs. PD (28): Optimal Separating Hyper-Plane (1 st, 2 nd and 3 rd components)

31 Application I > Parkinson s Disease LOO VALIDATION N/2 VALIDATION Performance evaluation > Accuracy, specificity and sensitivity rates of SVM Overall Mean Accuracy* (%) Overall Mean Specificity* (%) Overall Mean Sensitivity* (%) Accuracy >80 (%) Mean (Min/Max) Specificity >80 (%) Mean (Min/Max) Sensitivity >80 (%) Mean (Min/Max) PD vs. HC PSP vs. HC PSP vs. PD PD vs. HC PSP vs. HC PSP vs. PD (83.9/100.0) 92.3 (81.3/100.0) 93.4 (80.6/100.0) (92.9/100.0) 98.2 (92.9/100.0) 95.9 (90.0/100.0) (94.6/100.0) 98.8 (96.3/100.0) 97.8 (93.1/100.0) (89.3/100) 90.6 (82.4/100) 97.4 (92.3/100) (85.7/96.4) 92.5 (85.7/100) 92.4 (85.7/100) (89.3/96.4) 91.3 (82.4/100) 94.4 (86.7/100) * Obtained as mean values calculated over a number of PCA components ranging from 1 to the whole number of extracted PCA components Calculated over a range of PCA components for which Accuracy, Specificity and Sensitivity fell above 80%

32 Application I > Parkinson s Disease Accuracy, specificity and sensitivity rates (%) of SVM versus Number of PCA coefficients (PSP vs. PD, LOO validation)

33 Application I > Parkinson s Disease Diagnostic MR-related biomarkers Thalamus Midbrain (Substantia Nigra) Pons Corpus Callosum Corpus Callosum Thalamus Midbrain Pons C Salvatore et al. Journal of Neuroscience Methods (2015) Midbrain Pons Corpus Callosum Thalamus

34 Application I > Parkinson s Disease Diagnostic MR-related biomarkers PD versus Healthy Controls: medial part of the midbrain (encompassing the substantia nigra) and caudal part of the pons. These findings are consistent with the Braak s neuroanatomical model of the PD. PSP versus Healthy Controls: midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. PSP versus PD: midbrain, pons, corpus callosum and thalamus. These features are highly consistent with typical neuropathological and imaging findings described in patients with PSP, where a key role is played by the volumetric atrophy of the brainstem, which represents a hallmark of PSP.

35 Application II Alzheimer s Disease

36 Application II > Alzheimer s Disease Alzheimer s Disease (AD) Diagnosis Individual diagnosis of AD is predominantly based on the clinical examination and neuropsychological assessment Definite diagnosis can only be performed by post-mortem analysis Additional supportive features can be obtained by neuronal injury biomarkers as measured by neuroimaging studies, including MRI MR changes provide measurements of atrophic regions even before dementia is apparent Detecting the transitions asymptomatic phase -> symptomatic pre-dementia phase -> dementia onset in the clinical setting is a non-trivial issue This causes a diagnostic uncertainty for the early stage of disease Determination of sensitive and specific markers of very early AD progression

37 Application II > Alzheimer s Disease Cohort and images obtained from the Alzheimer s Disease Neuroimaging Initiative (ADNI) database Cohort 162 Healthy Control (CN) subjects 134 Mild Cognitive Impairment not converter to AD (MCInc) 076 Mild Cognitive Impairment converter to AD (MCIc) 137 Alzheimer s Disease (AD) Healthy control subjects of similar age and gender as both patients groups MR images T1-weighted structural MR images (1.5 T) A total of 509 scans from 41 different radioligical centers

38 Application II > Alzheimer s Disease Performance evaluation Nested k-fold cross validation: K-1 subsets for training + parameter estimation and optimization The remaining (1) subset for performance evaluation Multiple (N) rounds K = 20 N rounds PERFORMANCE EVALUATION TRAINING PARAMETER ESTIMATION AND OPTIMIZATION

39 Application II > Alzheimer s Disease Comparisons MCIc vs. MCInc MCIc vs. CN AD vs. CN

40 Application II > Alzheimer s Disease Normalization to MNI space by automatic elastic co-registration to the MNI template (MNI152_T1_1mm_brain)

41 Application II > Alzheimer s Disease PCA coefficients

42 Application II > Alzheimer s Disease Explained variance Before FDR ranking After FDR ranking

43 Application II > Alzheimer s Disease Optimal separating hyperplane

44 Application II > Alzheimer s Disease Parameter optimization AD vs. CN GM tissue probability map 10 mm 3 FWHM of isotropic Gaussian kernel (smoothing) 127 PCA coefficients Minimum error = 0.08 MCIc vs. CN GM tissue probability map 6 mm 3 FWHM of isotropic Gaussian kernel (smoothing) 67 PCA coefficients Minimum error = 0.14 MCIc vs. MCInc GM tissue probability map 2 mm 3 FWHM of isotropic Gaussian kernel (smoothing) 34 PCA coefficients Minimum error = 0.27

45 Application II > Alzheimer s Disease Performance evaluation Balanced accuracy AD vs. CN MCIc vs. CN MCIc vs. MCInc 0.76 ± ± ± 0.16

46 Application II > Alzheimer s Disease Temporal pole Medial temporal cortex (hippocampus and enthorinal cortex) AD vs CN Segmented GM 137 AD 162 CN Cerebellum (posterior lobule) Caudate, putamen Amygdala Thalamus Gyrus rectus Precuneus Anterior cingulate cortex Insular cortex C Salvatore et al. Frontiers in neuroscience (2015)

47 Application II > Alzheimer s Disease The major part of voxels influencing the classification of both MCIc vs. CN and MCIc vs. MCInc was similar to the one previously found in AD MCIc vs CN Segmented GM 76 MCIc 162 CN Is diagnosis of AD at very early stages by MRI a matter of sensitivity? C Salvatore et al. Frontiers in neuroscience (2015)

48 Application II > Alzheimer s Disease The major part of voxels influencing the classification of both MCIc vs. CN and MCIc vs. MCInc was similar to the one previously found in AD MCIc vs MCInc Segmented GM 76 MCIc 134 MCInc Is diagnosis of AD at very early stages by MRI a matter of sensitivity? C Salvatore et al. Frontiers in neuroscience (2015)

49 Application III Eating Disorders

50 Application III > Eating Disorders Eating Disorders (ED) Diagnosis To date, individual diagnosis of ED is based only on a clinical interview complemented by physical, psychopathological and behavioural examinations ED diagnosis is unstable, with clinical features changing over time, often switching from Anorexia (AN) to Bulimia (BN) However, there is little interest in a technique for the automatic diagnosis of ED because clinical examinations are able to diagnose it No valid biomarkers of ED are known

51 Application III > Eating Disorders Cohort 17 Healthy Control (HC) subjects 17 Eating Disorders (ED)patients 11 Bulimia Nervosa (BN) 06 Anorexia Nervosa (AN) Healthy control subjects of similar age, education and BMI as patients group Patients recruited and data acquired at the Institute of Neurology, University Magna Graecia, Catanzaro MR images MRI system (3 T) Discovery MR-750 GE Medical Systems, USA FOV = 48cm Slice thickness = 1mm TR = 13.7ms; TE = 9.2ms T1-weighted 3D dataset

52 Application III > Eating Disorders Performance evaluation K-fold Cross Validation: K-1 subsets for training The remaining (1) subset for testing K = 10 K = 20 Multiple (K) rounds N rounds TESTING TRAINING

53 Application II > Parkinson s Disease Comparison ED vs. CN

54 Application III > Eating Disorders Feature extraction + selection PCA coefficient FDR (#)

55 Application III > Eating Disorders Classification (training) ED (17) vs. HC (17): Optimal Separating Hyper-Plane (1 st, 2 nd and 3 rd components)

56 Application III > Eating Disorders Performance evaluation > K-fold cross validation Accuracy Specificity Sensitivity k = k =

57 Application III > Eating Disorders MEDIAL ORBITOFRONTAL CORTEX (part of the ventral limbic circuit) Identification of emotional significance of appetizing stimuli and regulation of inhibitory and reward systems CEREBELLUM Motor, cognitive and emotional functions, feeding behaviour, appetite regulation PRIMARY VISUAL CORTEX Body image disturbance

58 Scalability

59 Scalability of the implemented method GHz AD vs. CN dataset (299 subjects) MCIc vs. CN dataset (238 sbjs) MCIc vs. MCInc dataset (210 sbjs) from Application II described above Operational time required: by the whole pre-processing and training of the classifier (including feature extraction and selection) by the testing phase (including preprocessing and classification of the new dataset) 31.7s (AD vs. CN) 21.7s (MCIc vs. CN) 21.2s (MCIc vs. MCInc) 1.5s / subject on average

60 Scalability of the implemented method GHz AD vs. CN dataset (299 subjects) MCIc vs. CN dataset (238 sbjss) MCIc vs. MCInc dataset (210 sbjs) from Application II described above Operational time required: by the whole pre-processing and training of the classifier (including feature extraction and selection) by the testing phase (including preprocessing and classification of the new dataset) 31.7s (AD vs. CN) 21.7s (MCIc vs. CN) 21.2s (MCIc vs. MCInc) 1.5s / subject on average What would be the operational costs using larger (big data) datasets? What would be the operational costs in the daily clinical practice?

61 From bench to bedside Web-based service

62 Conclusions

63 Conclusions During my PhD, I have developed a supervised machinelearning method able to perform individual differential diagnosis of neurological (PD, PSP, AD) and psychiatric (ED) diseases by means of structural MR images Validation of this method returned classification performance comparable to or higher than other published methods We also implemented the extraction of pattern distribution maps of brain structural differences, which allowed the identification of possible MRI-related biomarkers useful for the diagnosis of the considered diseases Our findings offer new avenues for encouraging the application of computer-based diagnosis in clinical practice

64 Publications ISI International Papers Gallivanone, F., Canevari, C., Gianolli, L., Salvatore, C., Della Rosa, P. A., Gilardi, M. C., & Castiglioni, I. (2013). A partial volume effect correction tailored for 18 F-FDG-PET oncological studies. BioMed research international, Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M.,... & Quattrone, A. (2014). Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy. Journal of neuroscience methods, 222, Canini, M., Battista, P., Della Rosa, P. A., Catricalà, E., Salvatore, C., Gilardi, M. C., & Castiglioni, I. (2014). Computerized Neuropsychological Assessment in Aging: Testing Efficacy and Clinical Ecology of Different Interfaces. Computational and mathematical methods in medicine, Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I. (2015). Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. Journal of autism and developmental disorders, 45(7), doi: /s Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., & Castiglioni, I. (2015). Magnetic Resonance Imaging biomarkers for the early diagnosis of Alzheimer s Disease: a machine learning approach. Frontiers in Neuroscience. doi: /fnins Salvatore, C., Battista, P., & Castiglioni, I. (2015). Frontiers for the early diagnosis of AD by means of MRI brain imaging and Support Vector Machines. Current Alzheimer Research. [in press] Cerasa, A., Castiglioni, I., Salvatore, C., Funaro, A., Martino, I.,, Alfano, S., Donzuso, G., Perrotta, P., Gioia, M.C., Gilardi, M.C., & Quattrone, A. (2015). Individual detection of patients with eating disorders using support vector machine analysis of morphological imaging data: preliminary results. Behavioural Neurology. [minor revision] Battista, P., Salvatore, C., & Castiglioni, I. Neuropsychological testing coupled to machine learning algorithms allow the identification of a profile of markers for dementia. [submitted] Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., & Castiglioni, I. A web-based service to support the diagnosis of Alzheimer s Disease. [submitted]

65 Publications Indexed International Papers Grosso, E., López, M., Salvatore, C., Gallivanone, F., Di Grigoli, G., Valtorta, S.,... & Castiglioni, I. (2012, August). A Decision Support System for the assisted diagnosis of brain tumors: A feasibility study for 18 F-FDG PET preclinical studies. In Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE (pp ). IEEE. Gallivanone, F., Di Grigoli, G., Salvatore, C., Valtorta, S., Gilardi, M. C., Moresco, R. M., & Castiglioni, I. (2012, October). Acute stress studies in rats by 18 FDG PET and SPM. In Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2012 IEEE (pp ). IEEE. Cava, C., Zoppis, I., Mauri, G., Ripamonti, M., Gallivanone, F., Salvatore, C.,... & Castiglioni, I. (2013, July). Combination of gene expression and genome copy number alteration has a prognostic value for breast cancer. In Engineering in Medicine and Biology Society (EMBC), th Annual International Conference of the IEEE (pp ). IEEE. International Book Chapters 13 Cava, C., Gallivanone, F., Salvatore, C., Della Rosa, P. and Castiglioni, I. (2014). Bioinformatics clouds for high-throughput technologies. Handbook of Research on Cloud Infrastructures for Big Data Anaytics. IGI Global: doi: / ch020.

66 Publications International Conference Proceedings Gallivanone, F., Di Grigoli, G., Salvatore, C., Valtorta, S., Grosso, E., Gilardi, M.C., Moresco, R.M. and Castiglioni, I. (2012). SPM for activation studies in rats on stress conditions. 6th Hot Topics in Molecular Imaging Conference (TOPIM): p.44. Gallivanone, F., Di Grigoli, G., Salvatore, C., Belloli, S., Valtorta, S., Raccagni, I., Gilardi, M.C., Castiglioni, I. and Moresco, R.M. (2013). Feasibility of supervised machine learning technique for assisted diagnosis of cancer: application to PET studies in small animals. European Molecular Imaging Meeting (EMIM), 8th annual meeting of the European Society for Molecular Imaging (ESMI), Turin, Italy. Castiglioni, I., Cerasa, A., Salvatore, C., Gallivanone, F., Augimeri, A., Lopez, M.,... & Quattrone, A. (2013, June). Machine learning performs differential individual diagnosis of PD and PSP by brain MRI studies. In MOVEMENT DISORDERS (Vol. 28, pp. S71-S72). 111 RIVER ST, HOBOKEN , NJ USA: WILEY-BLACKWELL. Salvatore, C., Cerasa, A., Battista, P., Gilardi, M.C., Quattrone, A., & Castiglioni, I. (2015). Neuroimaging biomarkers predicting conversion to AD. 2nd meeting of the Scientific Council of NeuroMI, Milan. Battista, P., Salvatore, C., Gilardi, M.C., & Castiglioni, I. (2015). Neuropsychological testing and artificial intelligence for early and differential diagnosis of dementia. 2nd meeting of the Scientific Council of NeuroMI, Milan. National Conference Proceedings 19 Gallivanone, F., Grosso, E., Di Grigoli, G., Salvatore, C., Valtorta, S., Gilardi, M.C., Moresco, R.M. and Castiglioni, I. (2012). Statistical Parametric Mapping for activation studies in rats. Atti del Congresso Nazionale di Bioingegneria: p.147; ISBN:

67 Thank you for your attention!