{#sec:qualitative_measures}
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In order to collect, normalize, and analyze visual and verbal feedback given by the participants, a summative qualitative content analysis is conducted via abstraction [@hsieh2005three; @elo2008qualitative]. The abstraction method reduces the overall transcript material while preserving its substantial contents by summarization. The corpus retains a valid mapping of the recording. An essential part of abstraction is the formulation of macro operators like elimination, generalization, construction, integration, selection and bundling. The abstraction of statements is increased iteratively by the use of macro operators, which map statements of the current level of abstraction to the next, while clustering items based on their similarity [@mayring2014qualitative].

HCI Evaluation {#sec:hci_evaluation}
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A user study is the most precise method for the evaluation of the quality of different interactive segmentation approaches [@nickisch2010learning]. Analytical measures as well as subjective measures can be derived from standardized user tests [@gao2013mental]. From interaction data recorded during the study, the reproducibility of segmentation results as well as the achievable accuracy with a given system per time can be estimated. The complexity and novelty of the system can be expressed via the observed convergence to the ground truth over time spent by the participants segmenting multiple images each. The user’s 