CycleStack: Inferring periodic behavior via temporal sequence visualization in ultrasound video - Teng-Yok Lee; Chaudhuri, A.; Porikli, F.; Han-Wei Shen (2010 IEEE Pacific Visualization Symposium (PacificVis), 2010)
A range of well-known treatment methods for destroying tumor and similar harmful growth in human body utilizes the coherence between the inherently periodic movement of the affected body part and periodic respiratory signal of the patient, with the objective of minimizing damage to surrounding normal tissues. Such methods require constant monitoring by an operator who observes the 3D body motion via its 2D projection onto an ultrasound imaging plane and studies the synchronism of this motion with the respiratory signal. Keeping an attentive eye on the respiratory signal as well as the ultrasound video for the entire treatment period is often inconvenient and burdensome. In this paper, we propose a video visualization technique called CycleStack Plot which reduces this cognitive overhead by blending the video and the signal together in a stack-like layout. This visualization reveals the inherent synchronism between the target's movement and the respiratory signal, visually highlights significant phase shifts of either of the two cyclic phenomena, with the hope of arresting the operator's attention. Our proposed visualization also provides a visual overview for the post-treatment analysis which enables educated users to quickly and effectively skim through the excessively long process. This paper demonstrates the utility of CycleStack Plot with a case study using real ultrasound videos. In addition, a user study has been performed to evaluate the merits and limitations of the proposed method with respect to the conventional way of watching a video and a signal side-by-side. Even though the motivation of the proposed visualization is improvement of medical applications that use ultrasound, the core techniques discussed here have potential to be extended to other application domains requiring analysis of cyclic patterns from videos.