Abstract
In medical image segmentation, limited external validity remains a critical obstacle when models are deployed across unseen datasets, an issue particularly pronounced in the ultrasound image domain. Existing solutions-such as domain adaptation and GAN-based style transfer-while promising, often fall short in the medical domain where datasets are typically small and diverse. This paper presents a novel application of principal component analysis (PCA) to address this limitation. PCA preprocessing reduces noise and emphasizes essential features by retaining approximately 90\% of the dataset variance. We evaluate our approach across six diverse breast tumor ultrasound datasets comprising 3,983 B-mode images and corresponding expert tumor segmentation masks. For each dataset, a corresponding dimensionality reduced PCA-dataset is created and U-Net-based segmentation models are trained on each of the twelve datasets. Each model trained on an original dataset was inferenced on the remaining five out-of-domain original datasets (baseline results), while each model trained on a PCA dataset was inferenced on five out-of-domain PCA datasets. Our experimental results indicate that using PCA reconstructed datasets, instead of original images, improves the model's recall and Dice scores, particularly for model-dataset pairs where baseline performance was lowest, achieving statistically significant gains in recall (0.57 $\pm$ 0.07 vs. 0.70 $\pm$ 0.05, $p = 0.0004$) and Dice scores (0.50 $\pm$ 0.06 vs. 0.58 $\pm$ 0.06, $p = 0.03$). Our method reduced the decline in recall values due to external validation by $33\%$. These findings underscore the potential of PCA reconstruction as a safeguard to mitigate declines in segmentation performance, especially in challenging cases, with implications for enhancing external validity in real-world medical applications.
Abstract (translated)
在医学图像分割领域，模型部署到未见过的数据集时，有限的外部有效性仍然是一个关键障碍，尤其是在超声影像领域问题更为突出。虽然现有的解决方案（如域适应和基于GAN的风格迁移）很有前景，但在数据集通常较小且多样化的医疗领域中往往效果不佳。本文提出了一种新颖的应用主成分分析（PCA）的方法来解决这一限制。通过PCA预处理可以减少噪声，并通过保留大约90%的数据集方差来强调关键特征。 我们在六个不同的乳腺肿瘤超声图像数据集中评估了我们的方法，这些数据集包含3,983张B模式图像及其对应的专家绘制的肿瘤分割掩模。对于每个数据集，我们创建了一个相应的降维PCA数据集，并在十二个数据集中的每一个上训练基于U-Net的分割模型。每种原始数据集上的模型在其余五个不同的域外原始数据集上进行推理（基线结果），而使用PCA数据集训练的模型则在五组域外PCA数据集中进行推理。 我们的实验结果显示，与使用原始图像相比，采用PCA重构的数据集可以提高模型召回率和Dice分数，特别是在那些基线性能最低的模-数对中。这导致了显著提升的召回率（0.57 ± 0.07 对比 0.70 ± 0.05, p = 0.0004）和Dice分数（0.50 ± 0.06 对比 0.58 ± 0.06, p = 0.03）。我们的方法将由于外部验证导致的召回值下降减少了33%。 这些发现强调了PCA重建作为一种潜在措施，可以在挑战性案例中降低分割性能下降的程度，并对增强现实世界医疗应用中的外部有效性具有重要意义。
URL
https://arxiv.org/abs/2505.23587
