Accurate segmentation of anatomical structures and abnormalities in medical images is crucial for computer-aided diagnosis and analysis. While deep learning techniques excel at this task, their computational demands pose challenges. Additionally, some cutting-edge segmentation methods, though effective for general object segmentation, may not be optimised for medical images. To address these issues, we propose Mini-Net, a lightweight segmentation network specifically designed for medical images. With fewer than 38,000 parameters, Mini-Net efficiently captures both high- and low-frequency features, enabling real-time applications in various medical imaging scenarios. We evaluate Mini-Net on various datasets, including DRIVE, STARE, ISIC-2016, ISIC-2018, and MoNuSeg, demonstrating its robustness and good performance compared to state-of-the-art methods.

翻译：医学图像中解剖结构与异常区域的精确分割对于计算机辅助诊断与分析至关重要。尽管深度学习技术在此任务中表现出色，但其计算需求带来了挑战。此外，一些前沿的分割方法虽在通用目标分割中效果显著，却未必针对医学图像进行优化。为解决这些问题，我们提出了Mini-Net——一种专为医学图像设计的轻量化分割网络。Mini-Net参数量不足38,000个，能高效捕获高频与低频特征，可在多种医学影像场景中实现实时应用。我们在DRIVE、STARE、ISIC-2016、ISIC-2018及MoNuSeg等多个数据集上评估了Mini-Net，结果表明其相较于前沿方法具有鲁棒性及良好性能。