基于深度学习的有限且次优标注下兔胎颅骨分割
Deep learning-based Segmentation of Rabbit fetal skull with limited and sub-optimal annotations
AI总结：
针对兔胎micro-CT骨骼标注有限且次优的问题，提出利用26个标注体数据映射250个未标注体数据训练深度CNN，实现22块颅骨的高精度分割。
摘要：
本文提出一种基于深度学习的方法，用于分割荷兰带兔胎micro-CT图像中的骨骼结构，以辅助评估药物诱导的骨骼异常；这是发育与生殖毒理学（DART）中的一项必要研究。我们的策略利用来自26个micro-CT体数据中22块颅骨的次优分割标签，并将其映射到250个未标注体数据上，随后在这些数据上训练基于深度CNN的分割模型。实验中，该模型在测试集所有骨骼上取得平均Dice相似系数（DSC）0.89，且26块颅骨中有14块达到平均DSC>0.93。下一步将分割全身，并开发用于异常分类的模型。
英文摘要：
In this paper, we propose a deep learning-based method to segment the skeletal structures in the micro-CT images of Dutch-Belted rabbit fetuses which can assist in the assessment of drug-induced skeletal abnormalities as a required study in developmental and reproductive toxicology (DART). Our strategy leverages sub-optimal segmentation labels of 22 skull bones from 26 micro-CT volumes and maps them to 250 unlabeled volumes on which a deep CNN-based segmentation model is trained. In the experiments, our model was able to achieve an average Dice Similarity Coefficient (DSC) of 0.89 across all bones on the testing set, and 14 out of the 26 skull bones reached average DSC >0.93. Our next steps are segmenting the whole body followed by developing a model to classify abnormalities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
