Applied machine learning for liver surgery

Background and objectives: Both hepatic functional reserve and the underlying histology are important determinants in the preoperative risk evaluation before major hepatectomies. In this project we developed a new approach that implements cutting-edge research in machine learning and nevertheless is cheap and easily applicable in a routine clinical setting is needed. Methods: After splitting the study population into a training and test set we trained a convolutional neural network to predict the liver function as determined by hepatobiliary mebrofenin scintigraphy and single photon emission computer tomography (SPECT) imaging. Results: We developed a workflow for predicting liver function from routine CT imaging data using convolutional neural networks. We also evaluated in how far transfer learning and data augmentation can help to solve remaining manual data pre-processing steps and implemented the developed workflow in a clinical routine setting. Conclusion: We propose a robust semiautomatic end-to-end classification workflow for abdominal CT scans for the prediction of liver function based on a deep convolutional neural network model that shows reliable and accurate results even with limited computational resources.