论文标题
通过零件的两样本t检验增强损失的医学图像分割的高分辨率边界检测
High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss
论文作者
论文摘要
深度学习方法为医学图像分割的快速发展做出了重大贡献，其质量依赖于合适的损失功能设计。流行的损失功能，包括跨凝性和骰子损失，通常没有边界检测，从而限制了下游应用的高分辨率，例如自动诊断和程序。我们开发了一种新颖的损失函数，该功能是为反映边界信息而定制的，以增强边界检测。由于分割边界的分割和背景区域之间的对比自然会引起像素上的异质性，因此我们提出了零件的两样本t检验增强（PTA）损失（PTA）的损失，该损失被注入了此类异质性的统计测试。与没有t检验组件的基准损失相比，我们证明了PTA损失的边界检测能力的提高。
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.
