SegDT：一种基于扩散变换器的医学影像分割模型

摘要

医学图像分割在众多医疗任务中至关重要，包括疾病诊断与治疗规划。其中，皮肤病变分割作为关键领域，对于皮肤癌的诊断及患者监测具有重要意义。在此背景下，本文提出了一种基于扩散变换器（DiT）的新型分割模型——SegDT。SegDT专为低成本硬件设计，并引入了整流流（Rectified Flow）技术，该技术不仅能在减少推理步骤的同时提升生成质量，还保持了标准扩散模型的灵活性。我们的方法在三个基准数据集上进行了评估，并与多项现有工作进行了对比，结果显示其在保持快速推理速度的同时，达到了业界领先的精度。这使得所提出的模型在实际医疗应用中极具吸引力。本研究成果推动了深度学习模型在医学图像分析中的性能与能力，为医疗专业人员提供了更快、更精准的诊断工具。相关代码已公开于https://github.com/Bekhouche/SegDT{GitHub}。

English

Medical image segmentation is crucial for many healthcare tasks, including disease diagnosis and treatment planning. One key area is the segmentation of skin lesions, which is vital for diagnosing skin cancer and monitoring patients. In this context, this paper introduces SegDT, a new segmentation model based on diffusion transformer (DiT). SegDT is designed to work on low-cost hardware and incorporates Rectified Flow, which improves the generation quality at reduced inference steps and maintains the flexibility of standard diffusion models. Our method is evaluated on three benchmarking datasets and compared against several existing works, achieving state-of-the-art results while maintaining fast inference speeds. This makes the proposed model appealing for real-world medical applications. This work advances the performance and capabilities of deep learning models in medical image analysis, enabling faster, more accurate diagnostic tools for healthcare professionals. The code is made publicly available at https://github.com/Bekhouche/SegDT{GitHub}.

SegDT：一种基于扩散变换器的医学影像分割模型

SegDT: A Diffusion Transformer-Based Segmentation Model for Medical Imaging

摘要

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