User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy

Abstract

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians’ expertise and computers’ potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the “strokes” and the “contour”, to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.

Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients

“…It should therefore be noted that providing additional training to the user might allow a more efficient use of the interactive computerassisted system. These findings are supported by the work of Ramkumar et al, 35 in which dependencies between user interaction and segmentation performance of the proposed interactive approach by use of two different interactions input (seeds vs coarse contours) are investigated. Authors highlight that, besides the performance of the algorithm, the quality of the segmentation also depends on the user and the human computer interaction process.…”

State‐of‐the‐Art Report: Visual Computing in Radiation Therapy Planning

“…Three main sub‐topics can be regarded within this category: approaches aiding the segmentation of relevant structures, approaches enhancing the segmentation outcome by post‐processing and approaches assessing the outcome of the segmentation. All three subcategories incorporate user interaction with the segmentations, which has been discussed by Ramkumar et al [RDK*16].…”

A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems

“…1 make use of the insights about complex thought processes of a human utilizing an interactive segmentation system for the ranking of novel interactive segmentation methods. Ramkumar et al [25,26] acquire these data by well-designed questionnaires, but do not automate their evaluation method. We propose an automated, i. e. scalable, system to approximate pragmatic as well as hedonic usability aspects of a given interactive segmentation system.…”