Abstract
Accurate localization of organ boundaries is critical in medical imaging for segmentation, registration, surgical planning, and radiotherapy. While deep convolutional networks (ConvNets) have advanced general-purpose edge detection to near-human performance on natural images, their outputs often lack precise localization, a limitation that is particularly harmful in medical applications where millimeter-level accuracy is required. Building on a systematic analysis of ConvNet edge outputs, we propose a medically focused crisp edge detector that adapts a novel top-down backward refinement architecture to medical images (2D and volumetric). Our method progressively upsamples and fuses high-level semantic features with fine-grained low-level cues through a backward refinement pathway, producing high-resolution, well-localized organ boundaries. We further extend the design to handle anisotropic volumes by combining 2D slice-wise refinement with light 3D context aggregation to retain computational efficiency. Evaluations on several CT and MRI organ datasets demonstrate substantially improved boundary localization under strict criteria (boundary F-measure, Hausdorff distance) compared to baseline ConvNet detectors and contemporary medical edge/contour methods. Importantly, integrating our crisp edge maps into downstream pipelines yields consistent gains in organ segmentation (higher Dice scores, lower boundary errors), more accurate image registration, and improved delineation of lesions near organ interfaces. The proposed approach produces clinically valuable, crisp organ edges that materially enhance common medical-imaging tasks.
Abstract (translated)
准确地定位器官边界在医学成像中的分割、配准、手术规划和放射治疗等方面至关重要。虽然深度卷积网络（ConvNets）已经将一般用途的边缘检测提升到了接近人类水平的性能，尤其是在自然图像上，但它们的输出往往缺乏精确的定位能力，在要求毫米级精度的医疗应用中尤其有害。基于对ConvNet边缘输出的系统分析，我们提出了一种以医学为重点的清晰边缘探测器，该探测器采用新颖的自顶向下反向细化架构来处理医学影像（二维和体积图像）。我们的方法通过一个反向细化路径逐步上采样并融合高层次语义特征与低层次细节线索，从而产生高分辨率、精确定位的器官边界。进一步地，我们设计了一个扩展方案，以处理各向异性的体素数据，结合了2D切片级别的优化和轻量级3D上下文聚合，保留计算效率的同时改善效果。 在多个CT和MRI器官数据集上的评估表明，与基线ConvNet检测器及当前的医学边缘/轮廓方法相比，在严格的指标（边界F-measure、Hausdorff距离）下，我们的方法显著提高了边界的定位能力。重要的是，将我们提出的清晰边缘图集成到下游管道中，可以一致地提升器官分割性能（Dice分数更高，边界误差更低），提高图像配准的准确性，并改善邻近器官界面处病变区域的描绘精度。 所提出的方法生成了临床有价值的、清晰的器官边缘，显著增强了常见的医学成像任务。
URL
https://arxiv.org/abs/2508.06805
