LiverPlan: A Stage-Adaptive Immersive Visual Analytics Framework for Anatomical Liver Surgical Planning

2026-08-05

AI Summary

Existing two-dimensional desktop tools struggle to meet the demanding, multi-stage preoperative planning requirements of anatomical liver resection due to rigid interfaces, weak spatial perception, and fragmented safety metrics. To address these limitations, this work proposes LiverPlan—an immersive visualization framework tailored to the distinct cognitive demands of the three surgical planning phases. LiverPlan introduces, for the first time, a phase-specific adaptive visualization mechanism that explicitly embeds safety criteria through techniques such as context-preserving focus, hue-preserving rendering, direct 3D resection plane manipulation, and real-time safety feedback, thereby intuitively revealing vessel–plane relationships. In a controlled study with eight hepatobiliary surgeons, LiverPlan significantly reduced task completion time, lowered subjective cognitive load, and improved usability, demonstrating its effectiveness and innovative potential in complex surgical planning.

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.

Problem

• anatomical liver resection
• surgical planning
• visual analytics
• immersive visualization
• cognitive bottleneck

Innovation

• stage-adaptive visualization
• immersive analytics
• anatomical liver resection
• plane-vessel intersection
• cognitive workload reduction