: Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision. Within this context, we propose a method for classifying healthy, osteoporotic, and COVID-19 femoral heads SR-microCT images informing a vgg16 about the most subtle microscale differences using unsupervised patched-based clustering. Our strategy allows achieving up to 9.8% accuracy improvement in classifying healthy from osteoporotic images over uninformed methods, while 59.1% of accuracy between osteoporosis and COVID-19.Clinical relevance-We established a starting point for classifying healthy, osteoporotic, and COVID-19 femoral heads from SR-microCTs with human non-discriminative features, with 60.91% accuracy in healthy-osteporotic image classification.

Towards an Informed CNN for Bone SR-microCT Image Classification with an Unsupervised Patched-based Image Clustering

Poles, Isabella;D'Arnese, Eleonora;Buccino, Federica;Vergani, Laura;Santambrogio, Marco D
2023-01-01

Abstract

: Bone microscale differences cannot be readily recognizable to humans from Synchrotron Radiation micro-Computed Tomography (SR-microCT) images. Premises are possible with Deep Learning (DL) imaging analysis. Despite this, more attention to high-level features leads models to require help identifying relevant details to support a decision. Within this context, we propose a method for classifying healthy, osteoporotic, and COVID-19 femoral heads SR-microCT images informing a vgg16 about the most subtle microscale differences using unsupervised patched-based clustering. Our strategy allows achieving up to 9.8% accuracy improvement in classifying healthy from osteoporotic images over uninformed methods, while 59.1% of accuracy between osteoporosis and COVID-19.Clinical relevance-We established a starting point for classifying healthy, osteoporotic, and COVID-19 femoral heads from SR-microCTs with human non-discriminative features, with 60.91% accuracy in healthy-osteporotic image classification.
2023
2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
979-8-3503-2447-1
File in questo prodotto:
File Dimensione Formato  
Towards_an_Informed_CNN_for_Bone_SR-microCT_Image_Classification_with_an_Unsupervised_Patched-based_Image_Clustering.pdf

Accesso riservato

: Publisher’s version
Dimensione 1.85 MB
Formato Adobe PDF
1.85 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1257201
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? ND
social impact