Abstract
Despite considerable strides in developing deep learning models for 3D medical image segmentation, the challenge of effectively generalizing across diverse image distributions persists. While domain generalization is acknowledged as vital for robust application in clinical settings, the challenges stemming from training with a limited Field of View (FOV) remain unaddressed. This limitation leads to false predictions when applied to body regions beyond the FOV of the training data. In response to this problem, we propose a novel loss function that penalizes predictions in implausible body regions, applicable in both single-dataset and multi-dataset training schemes. It is realized with a Body Part Regression model that generates axial slice positional scores. Through comprehensive evaluation using a test set featuring varying FOVs, our approach demonstrates remarkable improvements in generalization capabilities. It effectively mitigates false positive tumor predictions up to 85% and significantly enhances overall segmentation performance.
Abstract (translated)
尽管在发展用于3D医疗图像分割的深度学习模型方面取得了显著的进步，但跨不同图像分布有效地进行泛化仍然具有挑战性。虽然领域泛化被认为是临床设置中稳健应用的关键，但训练受限视野（FOV）所带来的挑战仍未得到解决。这种局限导致在训练数据范围之外的身体区域应用时出现错误预测。为了解决这个问题，我们提出了一个新颖的损失函数，适用于单数据集和多数据集训练模式。通过使用身体部分回归模型生成轴向切片位置评分，在全面评估测试集中，我们的方法在泛化能力方面取得了显著的改进。它有效地将错误预测肿瘤降低至85%，并显著增强了整体分割性能。
URL
https://arxiv.org/abs/2404.15718
