Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

作者：de Moura Meneses Anderson Alvarenga; Gomes Pinheiro Christiano Jorge; Rancoita Paola; Schaul Tom; Gambardella Luca Maria; Schirru Roberto; Barroso Regina Cely; de Oliveira Luis Fernando

来源：Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 2010, 621(1-3): 662-669.

DOI：10.1016/j.nima.2010.05.022

• Synchrotron radiation
• Micro-computed tomography
• Histomorphometry
• Artificial neural networks
• Artificial intelligence

Micro-computed tomography (mu CT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on mu CT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-mu CT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-mu CT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-mu CT medical images.

• 出版日期2010-9