An Uncertainty-aware Hierarchical Probabilistic Network for Early Prediction, Quantification and Segmentation of Pulmonary Tumour Growth

TL;DR

This paper introduces a hierarchical probabilistic network that predicts, quantifies, and segments pulmonary tumour growth while estimating uncertainty, improving early detection and treatment planning.

Contribution

It presents a novel deep hierarchical generative framework that outperforms existing deterministic and probabilistic models in lung tumour growth prediction.

Findings

Balanced accuracy of 74% in tumour growth prediction

Tumour size MAE of 1.77 mm

Segmentation Dice score of 78%

Abstract

Early detection and quantification of tumour growth would help clinicians to prescribe more accurate treatments and provide better surgical planning. However, the multifactorial and heterogeneous nature of lung tumour progression hampers identification of growth patterns. In this study, we present a novel method based on a deep hierarchical generative and probabilistic framework that, according to radiological guidelines, predicts tumour growth, quantifies its size and provides a semantic appearance of the future nodule. Unlike previous deterministic solutions, the generative characteristic of our approach also allows us to estimate the uncertainty in the predictions, especially important for complex and doubtful cases. Results of evaluating this method on an independent test set reported a tumour growth balanced accuracy of 74%, a tumour growth size MAE of 1.77 mm and a tumour segmentation Dice score of 78%. These surpassed the performances of equivalent deterministic and alternative generative solutions (i.e. probabilistic U-Net, Bayesian test dropout and Pix2Pix GAN) confirming the suitability of our approach.