Abstract
Accurate polyp delineation in colonoscopy is crucial for assisting in diagnosis, guiding interventions, and treatments. However, current deep-learning approaches fall short due to integrity deficiency, which often manifests as missing lesion parts. This paper introduces the integrity concept in polyp segmentation at both macro and micro levels, aiming to alleviate integrity deficiency. Specifically, the model should distinguish entire polyps at the macro level and identify all components within polyps at the micro level. Our Integrity Capturing Polyp Segmentation (IC-PolypSeg) network utilizes lightweight backbones and 3 key components for integrity ameliorating: 1) Pixel-wise feature redistribution (PFR) module captures global spatial correlations across channels in the final semantic-rich encoder features. 2) Cross-stage pixel-wise feature redistribution (CPFR) module dynamically fuses high-level semantics and low-level spatial features to capture contextual information. 3) Coarse-to-fine calibration module combines PFR and CPFR modules to achieve precise boundary detection. Extensive experiments on 5 public datasets demonstrate that the proposed IC-PolypSeg outperforms 8 state-of-the-art methods in terms of higher precision and significantly improved computational efficiency with lower computational consumption. IC-PolypSeg-EF0 employs 300 times fewer parameters than PraNet while achieving a real-time processing speed of 235 FPS. Importantly, IC-PolypSeg reduces the false negative ratio on five datasets, meeting clinical requirements.
Abstract (translated)
在鼻镜检查中,准确的边界形成是协助诊断、指导干预和治疗的关键。然而,当前深度学习方法由于完整性不足而不足,这常常表现为 missing Lesion parts。本文介绍了在 macro 和 micro 级别上的完整性概念,旨在减轻完整性不足。具体来说,模型应该在 macro 级别上区分整个息肉,并在 micro 级别上识别息肉内部的所有组件。我们的完整性捕获息肉分割(IC-PolypSeg)网络使用轻量级骨架和三个关键组件来改善完整性:1)像素级特征重分配(PFR)模块捕获通道上的全局空间相关性,在最终的语义丰富的编码特征中。2)跨阶段像素级特征重分配(CPFR)模块动态地融合高层语义和低级别空间特征,以捕获上下文信息。3)粗到细校准模块将 PFR 和 CPFR 模块组合起来,以实现精确的边界检测。对 5 个公共数据集进行广泛的实验表明,所提出的 IC-PolypSeg 网络在更高的精度方面比 8 个先进的方法更好,同时减少了计算开销。IC-PolypSeg-EF0 使用比 PraNet 少 300 倍的参数,但实现了 235 FPS 的实时处理速度。重要的是,IC-PolypSeg 降低了 5 个数据集的 false negative 比率,符合临床要求。
URL
https://arxiv.org/abs/2309.08234
