Combining Good Old Random Forest and DeepLabv3+ for ISLES 2018 CT-Based Stroke Segmentation

摘要

Recent years' segmentation challenges on Ischemic Stroke Lesion Segmentation (ISLES) attracted great interest in the medical image computing domain, reflected in>80 citations of the 2017 summary article of the initial ISLES 2015 challenge [1]. While 2015-2017 ISLES challenges focussed on MRI images, the 2018 challenge takes into account clinical relevance of (perfusion) CT to triage stroke patients. Thus, from a methodological point of view, it is now to be analyzed whether and to what extent the 2015-2017 methods can be adapted to automated core lesion segmentation using acute stroke CT perfusion imaging. We strive to deliver a baseline for ISLES 2018 by using two well established machine learning-based segmentation approaches already applied for the initial ISLES 2015 challenge: random forest (RF) with classical hand-crafted image features (i.e. the most frequently used type of algorithm in ISLES 2015) and encoder-decoder-style convolutional neuronal networks (CNNs). In detail, for CNN-based segmentation, we employ the DeepLabv3+ architecture. The performance of the individual as well as a combination of the segmentation approaches is evaluated based on the ISLES 2018 training data set, and respective results are presented. Aiming at an ISLES 2018-specific performance baseline, we do neither make use of additional data other than the provided challenge data nor perform extensive data augmentation. The results highlight the potential to improve stroke lesion segmentation accuracy by combining RF and CNN information.