Keywords:
Performed at one institution, Not applicable, Metastases, Lymphoma, Cancer, Computer Applications-General, CT, Lymph nodes, Artificial Intelligence, Abdomen, Artificial Intelligence and Machine Learning
Authors:
T. Sakinis, H. B. Jenssen; Oslo/NO
DOI:
10.26044/ecr2020/C-14337
Purpose
Semi-automatic area and volumetric measurements of lymph nodes and tumors are known to be more reliable than caliper-based measurements [1-3]. They are however still laborious and not routine in clinical practice.
Deep learning-based tools using convolutional neural networks (CNNs) are considered state-of-the-art for automated image segmentation, but consistently accurate automated segmentation remains challenging due to the high variability of anatomy and pathology [4]. Manual or semi-automated tools that do not make use of CNNs remain standard for most segmentation tasks.
DeepGrow (DG) is an interactive segmentation approach that incorporates users' input when making predictions [5]. A single click is often sufficient for an accurate 2D segmentation, but additional input is possible in the form of additional click(s) and growing click-size. Computation time within milliseconds enables real-time interaction. Our purpose was to explore whether the segmentation of lymph nodes using DeepGrow is comparable to traditional measurements (Fig. 1). We have made an adaption of this approach freely available in the form of a web-based application [6]. The requirements are Google Chrome and a computer with a modern graphics card.