The paper introduces SEGSRNet, a novel framework that combines advanced super-resolution and segmentation techniques to address the challenge of precisely identifying surgical instruments in low-resolution stereo endoscopic images.
The super-resolution part of the model features:
The segmentation part utilizes the SPP-LinkNet-34 architecture, which employs an encoder-decoder structure with a Spatial Pyramid Pooling (SPP) block to enhance multi-scale input handling and improve segmentation accuracy and efficiency.
The proposed model is evaluated on two datasets from the MICCAI 2018 Robotic Scene Segmentation Sub-Challenge and the 2017 Robotic Instrument Segmentation Challenge. It outperforms current state-of-the-art models in both super-resolution and segmentation tasks, demonstrating its effectiveness in complex medical imaging applications.
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ข้อมูลเชิงลึกที่สำคัญจาก
by Mansoor Haya... ที่ arxiv.org 04-23-2024
https://arxiv.org/pdf/2404.13330.pdfสอบถามเพิ่มเติม