Veeraraghavan Harini, Miller James V
General Electric Research, 1 Research Circle, Niskayuna, NY, USA.
Proc IEEE Int Symp Biomed Imaging. 2011 Mar-Apr;2011:1645-1648. doi: 10.1109/ISBI.2011.5872719. Epub 2011 Jun 9.
Interactive techniques leverage the expert knowledge of users to produce accurate image segmentations. However, the segmentation accuracy varies with the users. Additionally, users may also require training with the algorithm and its exposed parameters to obtain the best segmentation with minimal effort. Our work combines active learning with interactive segmentation and (i) achieves as good accuracy compared to a fully user guided segmentation but with significantly lower number of user interactions (on average 50%), and (ii) achieves robust segmentation by reducing segmantation variability with user inputs. Our approach interacts with user to suggest gestures or seed point placements. We present extensive experimental evaluation of our results on two different publicly available datasets.
交互式技术利用用户的专业知识来生成准确的图像分割。然而，分割精度会因用户而异。此外，用户可能还需要对算法及其公开的参数进行训练，以便以最小的工作量获得最佳分割。我们的工作将主动学习与交互式分割相结合，（i）与完全由用户指导的分割相比，实现了同样好的精度，但用户交互次数显著减少（平均减少50%），并且（ii）通过减少用户输入导致的分割变异性来实现稳健的分割。我们的方法与用户交互以建议手势或种子点放置。我们在两个不同的公开可用数据集上对我们的结果进行了广泛的实验评估。
