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Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science
Computer Vision and Pattern Recognition · Submitted 7 Jun 2014
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases.
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The abstract, sentence by sentence
In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. | contribution 1.00 | 0.86 | 0.09 | asserted 0.99 |
| s1 | More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. | method 0.43 | 0.27 | 0.04 | asserted 1.00 |
| s2 | However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. | background 0.65 | 0.13 | 0.03 | asserted 0.96 |
| s3 | For these difficult cases the algorithm still needs additional user support. | background 0.61 | 0.14 | 0.04 | asserted 0.99 |
| s4 | However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. | gap 0.42 | 0.15 | 0.09 | asserted 0.87 |
| s5 | I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. | contribution 0.65 | 0.83 | 0.42 | asserted 0.87 |
| s6 | These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. | method 0.97 | 0.62 | 0.15 | asserted 1.00 |
| s7 | For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D. | method 0.79 | 0.93 | 0.12 | asserted 1.00 |
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The paper
Original abstract
In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.
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