Abstract
We introduce a novel segmentation-aware joint training framework called generative reinforcement network (GRN) that integrates segmentation loss feedback to optimize both image generation and segmentation performance in a single stage. An image enhancement technique called segmentation-guided enhancement (SGE) is also developed, where the generator produces images tailored specifically for the segmentation model. Two variants of GRN were also developed, including GRN for sample-efficient learning (GRN-SEL) and GRN for semi-supervised learning (GRN-SSL). GRN's performance was evaluated using a dataset of 69 fully annotated 3D ultrasound scans from 29 subjects. The annotations included six anatomical structures: dermis, superficial fat, superficial fascial membrane (SFM), deep fat, deep fascial membrane (DFM), and muscle. Our results show that GRN-SEL with SGE reduces labeling efforts by up to 70% while achieving a 1.98% improvement in the Dice Similarity Coefficient (DSC) compared to models trained on fully labeled datasets. GRN-SEL alone reduces labeling efforts by 60%, GRN-SSL with SGE decreases labeling requirements by 70%, and GRN-SSL alone by 60%, all while maintaining performance comparable to fully supervised models. These findings suggest the effectiveness of the GRN framework in optimizing segmentation performance with significantly less labeled data, offering a scalable and efficient solution for ultrasound image analysis and reducing the burdens associated with data annotation.
Abstract (translated)
我们介绍了一种称为生成强化网络（GRN）的新型分段感知联合训练框架，该框架将分割损失反馈集成到单个阶段中，以同时优化图像生成和分割性能。还开发了一种名为分段引导增强（SGE）的图像增强技术，在此技术中，生成器会针对特定的分割模型产生专门定制的图像。此外，还开发了GRN的两种变体：用于样本高效学习的GRN (GRN-SEL) 和用于半监督学习的GRN (GRN-SSL)。 GRN 的性能使用来自29名受试者的69份完整注释3D超声扫描数据集进行了评估，这些注释包括六个解剖结构：真皮、浅层脂肪、浅层筋膜膜（SFM）、深层脂肪、深层筋膜膜（DFM）和肌肉。我们的研究结果表明，在与在完全标注的数据集上训练的模型相比，带有SGE的GRN-SEL能够将标记工作量减少高达70%，同时使Dice相似性系数（DSC）提高了1.98%。GRN-SEL本身可以减少60%的标签努力，而带有SGE的GRN-SSL减少了70%的标注需求，单独的GRN-SSL则减少了60%的需求，所有这些情况均保持了与完全监督模型相当的表现水平。 这些发现表明，GRN框架在使用显著较少的标记数据优化分割性能方面非常有效，并为超声图像分析提供了可扩展且高效的解决方案，同时减轻了数据标注相关的负担。
URL
https://arxiv.org/abs/2501.17690
