PDF(Pubmed)
Abstract：
Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.
摘要：
子宫肌瘤影响70％的育龄妇女，可能会影响他们的生育能力和健康。手动电影阅读通常用于识别子宫肌瘤，但这很耗时，辛苦,和主观。临床治疗需要考虑子宫壁之间的位置关系，子宫腔,和子宫肌瘤.然而,由于它们复杂多变的形状，邻近组织或器官的低对比度，和难以区分的边缘，在MRI中准确识别它们是困难的。我们的工作通过提出一个能够自动输出位置的实例分割网络来解决这些挑战，类别,每个器官和病变的面具。具体来说,我们设计了一个新的主干，它有助于学习对象多样性的形状特征，并滤除背景噪声干扰。我们优化了锚盒生成策略，以提供更好的先验，以增强边界盒预测和回归的过程。自适应迭代细分策略确保对象的掩模边界细节更加真实和准确。我们进行了大量的实验来验证我们的网络，与最先进的实例分割模型相比，实现了更好的平均精度(AP)结果。与基线网络相比，我们的模型改进了子宫壁上的AP，子宫腔,肌瘤减少8.8%，8.4%,和3.2%，分别。我们的工作是第一个在子宫MRI中实现多类实例分割，为临床制定合适的手术方案提供了方便客观的参考,对提高诊断效率,实现子宫肌瘤的自动辅助诊断具有重要价值。
