Computer-assisted liver graft steatosis assessment via learning-based texture analysis

Abstract

Purpose Fast and accurate graft hepaticsteatosis (HS) assessment is of primary importance for lowering liver-dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the goldstandard 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 machinelearning 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 twenty to discarded livers. Fifteen randomly-selected liver patches were extracted from each image. Patch size was 100×100. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (IN T ), histogram of local binary pattern (H LBP riu2 ), and graylevel co-occurrence matrix (F GLCM ) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semi-supervised 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 LBP riu2 +IN T +Blo) and semi-supervised 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.

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

“…This may be attributed to the ability of the SVM of handling high-dimensional feature-space (which was high if compared with the number of subjects in the LDV-beat dataset) and its robustness of tackling the noise components of the LDV signal. Similar conclusions have already been drawn in closer research fields [ 55 , 56 , 57 ].…”

Use of Artificial Intelligence as an Innovative Method for Liver Graft Macrosteatosis Assessment

“…Indeed, the inclusion of donor features in the algorithm helped increase the Acc of the classification by SVM‐SIL, as previously demonstrated. ( 14 ) The real innovation of this study, through AI computerization of a human process, is 3‐fold: first, to translate the subjective visual assessment of liver texture into an objective and standardized method (smartphone picture); second, to develop a fully automated HS assessment by the liver graft image extraction; and third, to replicate the clinical experience of a transplant surgeon (donor data analysis). A major limitation of this study is the small sample size, which is a common problem within the computer‐assisted diagnosis community, ( 20 ) particularly in the LT setting wherein a larger cohort of discarded grafts (without any intention to be transplanted) are available only for machine perfusion studies.…”

“…In our previous study ( 14 ) a set of images was created by manually cropping the original photographs so the target organ (liver) would occupy 100% of the frame with elimination of the background. Contrary to this method, in this study, a FCNN was used that was composed of several layers as shown in Fig.…”

Enhanced Vision to Improve Safety in Robotic Surgery

“…Textural features include local binary pattern [43], gray-level co-occurrence matrix [44], and histogram of oriented gradients [45]. This class of features has been successfully used for several applications, such as tissue classification in gastric [46] and colorectal images [47,48].…”