Title: Progressive and Multi-Path Holistically Nested Neural Networks for Pathological Lung Segmentation from CT Images,
Abstract: Pathological lung segmentation (PLS) is an important, yet challenging,
medical image application due to the wide variability of pathological lung
appearance and shape. Because PLS is often a pre-requisite for other imaging
analytics, methodological simplicity and generality are key factors in
usability. Along those lines, we present a bottom-up deep-learning based
approach that is expressive enough to handle variations in appearance, while
remaining unaffected by any variations in shape. We incorporate the deeply
supervised learning framework, but enhance it with a simple, yet effective,
progressive multi-path scheme, which more reliably merges outputs from
different network stages. The result is a deep model able to produce finer
detailed masks, which we call progressive holistically-nested networks
(P-HNNs). Using extensive cross-validation, our method is tested on
multi-institutional datasets comprising 929 CT scans (848 publicly available),
of pathological lungs, reporting mean dice scores of 0.985 and demonstrating
significant qualitative and quantitative improvements over state-of-the art
approaches.