LiverPlan: A Stage-Adaptive Immersive Visual Analytics Framework for Anatomical Liver Surgical Planning
Authors
Qixuan Liu (CUHK), Shi Qiu (CUHK), Xiwen Wu (Zhujiang Hospital, Southern Medical University), Yuqi Tong (The Chinese Unityversity of Hong Kong), Yinqiao Wang (CUHK), Ruiyang Li (The Chinese University of Hong Kong), Jialun Pei (The Chinese University of Hong Kong), Shengdong Zhao (City University of Hong Kong), Chi-Wing Fu (The Chinese University of Hong Kong), Pheng Ann Heng (The Chinese University of Hong Kong)
Presentation
- Session
- This feels amazingly real!
- Time
- Friday, Nov 13, 09:00 – 09:12 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall America north
Links
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Keywords
Liver surgical planning, stage-adaptive visualization, immersive visual analytics
Abstract
Anatomical liver resection (ALR) surgery is the most important treatment for liver cancer, yet preoperative planning demands complex, multi-stage clinical reasoning under competing safety constraints. Current 2D desktop tools are not well equipped to support this process, exhibiting three fundamental limitations: reliance on monolithic interfaces that fail to adapt to the distinct cognitive demands of each planning stage; a perceptual bottleneck caused by limited anatomical spatial representation and missing plane-vessel intersection visualization; and an attention bottleneck stemming from fragmented critical safety criteria display across separate views. We present LiverPlan, a stage-adaptive immersive visual analytics framework for ALR planning, grounded in an 8-month collaboration with two expert hepatobiliary surgeons. Decomposing the surgical planning process into three sequential yet cognitively distinct stages, LiverPlan externalizes the cognitive demand of each stage via tailored techniques: (1) context-preserving focus and hue-preserving rendering for anatomical discovery; (2) direct 3D resection plane manipulation coupled with real-time, embedded visual feedback on critical safety criteria during plan refinement; and (3) explicit plane-vessel intersection visualization for anticipatory surgery preparation. A within-subjects study with eight hepatobiliary surgeons against a desktop baseline shows large-effect-size improvements in task completion time, perceived cognitive workload, and system usability on controlled planning tasks. Moreover, our study reveals broader insights: LiverPlan reduces cognitive burden and encourages a shift in surgeons from merely satisfying safety criteria to actively optimizing them, suggesting that explicit visualization of spatial relationships lowers the cognitive barrier to complex surgical planning.
For Practitioners
Practitioners who may benefit from this paper include hepatobiliary surgeons and surgical trainees, and developers of medical visualization, XR, and clinical decision-support systems. Surgeons and trainees can use the three-stage framework to structure preoperative reasoning and training. Visualization, XR, and clinical decision-support developers can adapt the direct 3D manipulation, embedded safety feedback, and stage-adaptive interface design to reduce cognitive load and context switching in other complex clinical workflows.