Abstract
Image segmentation is pivotal in medical image analysis, facilitating clinical diagnosis, treatment planning, and disease evaluation. Deep learning has significantly advanced automatic segmentation methodologies by providing superior modeling capability for complex structures and fine-grained anatomical regions. However, medical images often suffer from data imbalance issues, such as large volume disparities among organs or tissues, and uneven sample distributions across different anatomical structures. This imbalance tends to bias the model toward larger organs or more frequently represented structures, while overlooking smaller or less represented structures, thereby affecting the segmentation accuracy and robustness. To address these challenges, we proposed a novel contour-weighted segmentation approach, which improves the model's capability to represent small and underrepresented structures. We developed PDANet, a lightweight and efficient segmentation network based on a partial decoder mechanism. We evaluated our method using three prominent public datasets. The experimental results show that our methodology excelled in three distinct tasks: segmenting multiple abdominal organs, brain tumors, and pelvic bone fragments with injuries. It consistently outperformed nine state-of-the-art methods. Moreover, the proposed contour-weighted strategy improved segmentation for other comparison methods across the three datasets, yielding average enhancements in Dice scores of 2.32%, 1.67%, and 3.60%, respectively. These results demonstrate that our contour-weighted segmentation method surpassed current leading approaches in both accuracy and robustness. As a model-independent strategy, it can seamlessly fit various segmentation frameworks, enhancing their performance. This flexibility highlighted its practical importance and potential for broad use in medical image analysis.
Abstract (translated)
图像分割在医学影像分析中至关重要，它有助于临床诊断、治疗计划制定以及疾病评估。深度学习通过提供对复杂结构和细微解剖区域的卓越建模能力，在自动分割方法上取得了显著进展。然而，医学图像常常受到数据不平衡问题的影响，例如不同器官或组织之间的体积差异较大，以及在不同解剖结构之间样本分布不均等。这种不平衡会导致模型偏向于更大的器官或更频繁出现的结构，而忽略较小或较少代表的结构，从而影响分割的准确性和鲁棒性。 为了解决这些问题，我们提出了一种新颖的轮廓加权分割方法，这种方法能够提升模型表示小规模和代表性较弱结构的能力。为此，我们开发了PDANet——一种基于部分解码机制的轻量级高效分割网络。我们在三个著名的公开数据集上评估了我们的方法。实验结果表明，在多器官腹部图像、脑肿瘤以及骨盆骨折碎片的分割这三项不同任务中，我们的方法均优于九种当前最先进的技术。此外，所提出的轮廓加权策略在上述所有对比方法中都带来了显著改进：分别提高了Dice分数平均值2.32%、1.67%和3.60%。这些结果表明，在准确性和鲁棒性方面，我们提出的轮廓加权分割法已经超越了当前的领先方案。 作为一种与模型无关的战略，它可以无缝地应用于各种分割框架中以提升其性能。这种灵活性突显了它在医学影像分析中的实用价值及其广泛的应用潜力。
URL
https://arxiv.org/abs/2601.14338
