Stacked autoencoder for segmentation of bone marrow histological images

Abstract

Stacked autoencoder was used for the segmentation of trabeculas from bone marrow histological images derived from patients after hip joints arthroplasty. Additional filtering of areas smaller than 20000 pixels is necessary. The method has 95% efficiency. Proposed stacked autoencoder processes input images without special intervention automatically that is the main advantage of unsupervised learning over the supervised learning.