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

• URL: http://arxiv.org/abs/2305.17871v1
• Date: Mon, 29 May 2023 03:24:02 GMT
• Title: propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
• Authors: Zifan Chen, Jiazheng Li, Jie Zhao, Yiting Liu, Hongfeng Li, Bin Dong, Lei Tang, Li Zhang
• Abstract summary: This study introduces a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency.
• Score: 16.135854257728337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: **Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for 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, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. **Methods:** We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. **Results:** With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. **Conclusions:** Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance.

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