Automatic Myelofibrosis Grading from Silver-Stained Images

Histopathology studies the tissues to provide evidence of a disease, type and grade. Usually, the interpretation of these tissue specimens is performed under a microscope by human experts, but since the advent of digital pathology, the slides are digitised, shared and viewed remotely, facilitating diagnosis, prognosis and treatment planning. Furthermore, digital slides can be analysed automatically with computer vision methods to provide diagnostic support, reduce subjectivity and improve efficiency. This field has attracted many researchers in recent years who mainly focused on the analysis of cells morphology on Hematoxylin & Eosin stained samples. In this work, instead, we focused on the analysis of reticulin fibres from silver stained images. This task has been addressed rarely in the literature, mainly due to the total absence of public data sets, but it is beneficial to assess the presence of fibrotic degeneration. One of them is myelofibrosis, characterised by an excess of fibrous tissue. Here we propose an automated method to grade myelofibrosis from image patches. We evaluated different Convolutional Neural Networks for this purpose, and the obtained results demonstrate that myelofibrosis can be identified and graded automatically.