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Erschienen in:
23.05.2018 | Original Article
Computer-assisted liver graft steatosis assessment via learning-based texture analysis
Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 9/2018
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Abstract
Purpose
Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process.
Methods
Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was \(100\times 100\). This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern (\(H_{{\mathrm{LBP}}_{riu2}}\)), and gray-level co-occurrence matrix (\(F_{\mathrm{GLCM}}\)) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance.
Results
With the best-performing feature set (\(H_{{\mathrm{LBP}}_{riu2}}+\hbox {INT}+\hbox {Blo}\)) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively.
Conclusions
This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR.