针对低场儿童脑影像，实现自动质量评估与海马分割。
Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images
- 用外观仿真生成伪影，结合DenseNet提升图像质量判断准确率。
- 海马分割采用平均模板配准，Dice系数达0.61。
- 适用于资源有限地区，为儿童脑发育研究提供基础工具。
了解儿童大脑的结构发育是识别多种神经发育障碍的关键步骤。然而，由于缺乏自动化图像分析工具，尤其在低收入和中等收入国家，高场强磁共振图像难以获取，限制了相关研究进展。低场磁共振系统正逐渐被这些国家采纳，因此亟需开发适用于此类图像的自动化分析工具。本研究作为初步探索，聚焦两项任务：1）自动化质量保证；2）海马体分割，并对比多种方法。在质量保证任务中，结合外观仿真的DenseNet模型表现最佳，加权准确率达82.3%。在分割任务中，基于平均模板配准的方法效果最优，最终Dice分数为0.61。结果表明，尽管低场图像可支持对大型病灶及宏观解剖发育的理解，但在更精细分析方面仍存在挑战。
原文摘要 · Abstract (English)
Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses.
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