Stacked sparse autoencoder networks and statistical shape models for automatic staging of distal femur trochlear dysplasia

Background The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella‐femoral joint. In this paper, we integrated statistical sh...

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Vydané v:The international journal of medical robotics + computer assisted surgery Ročník 14; číslo 6; s. e1947 - n/a
Hlavní autori: Cerveri, Pietro, Belfatto, Antonella, Baroni, Guido, Manzotti, Alfonso
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Wiley Subscription Services, Inc 01.12.2018
Predmet:
Abnormalities
Accuracy
Aged
Bone Diseases - diagnostic imaging
Bone Diseases - surgery
Cadaver
Classification
Computed tomography
Databases, Factual
Discriminant analysis
Femur
Femur - diagnostic imaging
Femur - surgery
Humans
Image Processing, Computer-Assisted
Imaging, Three-Dimensional
Joint Instability
Knee
Knee Joint - diagnostic imaging
Knee Joint - surgery
Machine learning
Mathematical models
Mathematical morphology
Middle Aged
Models, Statistical
Morphing
Parameters
Retrospective Studies
Set theory
sparse autoencoder networks
statistical shape modelling
Tomography, X-Ray Computed
trochlear dysplasia
trochlear morphology
sparse autoencoder networks
trochlear morphology
trochlear dysplasia
statistical shape modelling
ISSN:1478-5951, 1478-596X, 1478-596X
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Shrnutí:Background The quantitative morphological analysis of the trochlear region in the distal femur and the precise staging of the potential dysplastic condition constitute a key point for the use of personalized treatment options for the patella‐femoral joint. In this paper, we integrated statistical shape models (SSM), able to represent the individual morphology of the trochlea by means of a set of parameters and stacked sparse autoencoder (SSPA) networks, which exploit the parameters to discriminate among different levels of abnormalities. Methods Two datasets of distal femur reconstructions were obtained from CT scans, including pathologic and physiologic shapes. Both of them were processed to compute SSM of healthy and dysplastic trochlear regions. The parameters obtained by the 3D‐3D reconstruction of a femur shape were fed into a trained SSPA classifier to automatically establish the membership to one of three clinical conditions, namely, healthy, mild dysplasia, and severe dysplasia of the trochlea. The validation was performed on a subset of the shapes not used in the construction of the SSM, by verifying the occurrence of a correct classification. Results A major finding of the work is that SSM are able to represent anomalies of the trochlear geometry by means of specific eigenmodes of variation and to model the interplay between morphologic features related to dysplasia. Exploiting the patient‐specific morphing parameters of SSM, computed by means of a 3D‐3D reconstruction, SSPA is demonstrated to outperform traditional discriminant analysis in classifying healthy, mild, and severe trochlear dysplasia providing 99%, 97%, and 98% accuracy for each of the three classes, respectively (discriminant analysis accuracy: 85%, 89%, and 77%). Conclusions From a clinical point of view, this paper contributes to support the increasing role of SSM, integrated with deep learning techniques, in diagnostics and therapy definition as quantitative and advanced visualization tools.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1478-5951
1478-596X
1478-596X
DOI:10.1002/rcs.1947