Sketch‐Based Editing Tools for Tumour Segmentation in 3D Medical Images

Heckel, Frank; Moltz, Jan Hendrik; Tietjen, Christian; Hahn, Horst K.

doi:10.1111/cgf.12193

Cited by 31 publications

(26 citation statements)

References 51 publications

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“…Contouring tools can be categorized as fully automatic, semi-automatic or fully manual (Heckel et al 2013;Ramkumar et al 2016) based on the intended level of involvement of the physician and computation. Fully automatic contouring is potentially the most time efficient as it is designed to have little involvement of the physician.…”

Section: Contouring In Radiotherapy Planningmentioning

confidence: 99%

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The influence of automation on tumor contouring

et al. 2017

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Fully or semi-automatic contouring tools are increasingly being used in the tumor contouring task for radiotherapy. While the fully automatic contouring tools have not reached sufficient efficiency, the semi-automatic contouring tools balance more effectively between the human interaction and automation. This study evaluates the influences of a semi-automation contouring tool, called between-slice interpolation, on the resulting contours and the contouring process. The tumor contouring study was conducted on three patient cases with five physicians in a naturalistic setting. The contouring task consisted of initiating the 2D contour manually or with the interpolation tool and correcting that initial contour. The similarity of the resulting contours was pairwise measured within the manual or the interpolated category. Interactions with the software were recorded, and variations in the contouring workflows steps were compared. Results indicated that using the between-slice interpolation tool for creating the initial contour, instead of initiating it manually, influenced both the contouring process and outcomes. First, it was identified that contours initiated by the interpolation tool showed an increased similarity among themselves compared to the manually initiated contours. At the same time, influences to the resulting contours were below clinical relevance, and toward the desired direction-improved consistency of contours. Second, when interpolation was used, in two cases out of three, the average contouring time also decreased significantly. Therefore, the use of such an automation tool can be encouraged.

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Section: Contouring In Radiotherapy Planningmentioning

confidence: 99%

“…Physicians play a central role in steering and correcting the outcomes of the contouring task (Heckel et al 2013). …”

Section: Contouring In Radiotherapy Planningmentioning

confidence: 99%

The influence of automation on tumor contouring

et al. 2017

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“…However, a general conclusion on their accuracies is difficult to make, as they have been evaluated on individual cases [42]. Furthermore, most of those algorithms still require human involvement [43]. With the development of automatic segmentation algorithms, it can be foreseen that the GTV contouring task will gradually change to be a task of evaluating and correcting the outcome from computational results (contours).…”

Section: The Case Studymentioning

confidence: 99%

Using a contextualized sensemaking model for interaction design: A case study of tumor contouring

Aselmaa

Herk

Laprie

et al. 2017

Journal of Biomedical Informatics

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a b s t r a c tSensemaking theories help designers understand the cognitive processes of a user when he/she performs a complicated task. This paper introduces a two-step approach of incorporating sensemaking support within the design of health information systems by: (1) modeling the sensemaking process of physicians while performing a task, and (2) identifying software interaction design requirements that support sensemaking based on this model. The two-step approach is presented based on a case study of the tumor contouring clinical task for radiotherapy planning. In the first step of the approach, a contextualized sensemaking model was developed to describe the sensemaking process based on the goal, the workflow and the context of the task. In the second step, based on a research software prototype, an experiment was conducted where three contouring tasks were performed by eight physicians respectively. Four types of navigation interactions and five types of interaction sequence patterns were identified by analyzing the gathered interaction log data from those twenty-four cases. Further in-depth study on each of the navigation interactions and interaction sequence patterns in relation to the contextualized sensemaking model revealed five main areas for design improvements to increase sensemaking support. Outcomes of the case study indicate that the proposed two-step approach was beneficial for gaining a deeper understanding of the sensemaking process during the task, as well as for identifying design requirements for better sensemaking support.

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“…By analogy to interactive segmentation algorithms, dedicated editing tools can modify the segmentation result in 2-D [14][15][16] or 3-D. [17][18][19][20][21][22][23][24] We have previously given a detailed overview on segmentation editing for medical imaging. 25 Note that even though an automatic segmentation algorithm might be used in the first stage, the segmentation process itself becomes interactive from the user's point of view as soon as an intervention, such as editing in whatever form, is required. Adapting parameters of the automatic segmentation algorithm is not considered as editing in the sense of this paper, though.…”

mentioning

confidence: 99%

“…sketch-based segmentation editing tools that we have proposed earlier. 25 Nevertheless, the evaluation methods proposed in this paper can be used for other scenarios, other imaging modalities, and other segmentation editing tools as well, as long as they can be represented by the stepwise process shown in Fig. 2.…”

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confidence: 99%

On the evaluation of segmentation editing tools

et al. 2014

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Abstract. Efficient segmentation editing tools are important components in the segmentation process, as no automatic methods exist that always generate sufficient results. Evaluating segmentation editing algorithms is challenging, because their quality depends on the user's subjective impression. So far, no established methods for an objective, comprehensive evaluation of such tools exist and, particularly, intermediate segmentation results are not taken into account. We discuss the evaluation of editing algorithms in the context of tumor segmentation in computed tomography. We propose a rating scheme to qualitatively measure the accuracy and efficiency of editing tools in user studies. In order to objectively summarize the overall quality, we propose two scores based on the subjective rating and the quantified segmentation quality over time. Finally, a simulation-based evaluation approach is discussed, which allows a more reproducible evaluation without the need for human input. This automated evaluation complements user studies, allowing a more convincing evaluation, particularly during development, where frequent user studies are not possible. The proposed methods have been used to evaluate two dedicated editing algorithms on 131 representative tumor segmentations. We show how the comparison of editing algorithms benefits from the proposed methods. Our results also show the correlation of the suggested quality score with the qualitative ratings.

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Sketch‐Based Editing Tools for Tumour Segmentation in 3D Medical Images

Cited by 31 publications

References 51 publications

The influence of automation on tumor contouring

The influence of automation on tumor contouring

Using a contextualized sensemaking model for interaction design: A case study of tumor contouring

On the evaluation of segmentation editing tools

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