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**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotalfor diagnosis and treatment. The challenges lie in the irregular shapes,blurred boundaries of tumors, and the inefficiency of existing methods.  **Purpose:** We conducted a study to introduce a model, utilizinghuman-guided knowledge and unique modules, to address the challenges of 3Dtumor segmentation.  **Methods:** We developed the PropNet framework, propagating radiologists'knowledge from 2D annotations to the entire 3D space. This model consists of aproposing stage for coarse segmentation and a refining stage for improvedsegmentation, using two-way branches for enhanced performance and an up-downstrategy for efficiency.  **Results:** With 98 patient scans for training and 30 for validation, ourmethod achieves a significant agreement with manual annotation (Dice of 0.803)and improves efficiency. The performance is comparable in different scenariosand with various radiologists' annotations (Dice between 0.785 and 0.803).Moreover, the model shows improved prognostic prediction performance (C-indexof 0.620 vs. 0.576) on an independent validation set of 42 patients withadvanced gastric cancer.  **Conclusions:** Our model generates accurate tumor segmentation efficientlyand stably, improving prognostic performance and reducing high-throughput imagereading workload. This model can accelerate the quantitative analysis ofgastric tumors and enhance downstream task performance.

propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans