Purpose To develop a deep learning model for multi-anatomy and many-class
segmentation of diverse anatomic structures on MRI imaging.
Materials and Methods In this retrospective study, two datasets were curated
and annotated for model development and evaluation. An internal dataset of 1022
MRI sequences from various clinical sites within a health system and an
external dataset of 264 MRI sequences from an independent imaging center were
collected. In both datasets, 49 anatomic structures were annotated as the
ground truth. The internal dataset was divided into training, validation, and
test sets and used to train and evaluate an nnU-Net model. The external dataset
was used to evaluate nnU-Net model generalizability and performance in all
classes on independent imaging data. Dice scores were calculated to evaluate
model segmentation performance.
Results The model achieved an average Dice score of 0.801 on the internal
test set, and an average score of 0.814 on the complete external dataset across
49 classes.
Conclusion The developed model achieves robust and generalizable segmentation
of 49 anatomic structures on MRI imaging. A future direction is focused on the
incorporation of additional anatomic regions and structures into the datasets
and model.

通过开发深度学习模型，实现 MRI 图像上多解剖结构和众多类别的分割。

MRAnnotator: 一个用于 MRI 分割的多解剖结构深度学习模型

MRAnnotator: A Multi-Anatomy Deep Learning Model for MRI Segmentation

Short axis cardiac MRI segmentation is a well-researched topic, with
excellent results achieved by state-of-the-art models in a supervised setting.
However, annotating MRI volumes is time-consuming and expensive. Many different
approaches (e.g. transfer learning, data augmentation, few-shot learning, etc.)
have emerged in an effort to use fewer annotated data and still achieve similar
performance as a fully supervised model. Nevertheless, to the best of our
knowledge, none of these works focus on which slices of MRI volumes are most
important to annotate for yielding the best segmentation results. In this
paper, we investigate the effects of training with sparse volumes, i.e.
reducing the number of cases annotated, and sparse annotations, i.e. reducing
the number of slices annotated per case. We evaluate the segmentation
performance using the state-of-the-art nnU-Net model on two public datasets to
identify which slices are the most important to annotate. We have shown that
training on a significantly reduced dataset (48 annotated volumes) can give a
Dice score greater than 0.85 and results comparable to using the full dataset
(160 and 240 volumes for each dataset respectively). In general, training on
more slice annotations provides more valuable information compared to training
on more volumes. Further, annotating slices from the middle of volumes yields
the most beneficial results in terms of segmentation performance, and the
apical region the worst. When evaluating the trade-off between annotating
volumes against slices, annotating as many slices as possible instead of
annotating more volumes is a better strategy.

通过稀疏注释的训练，可以实现在体积占比显著减少的情况下（仅 48 个注释体积），得到 Dice 分数大于 0.85 且与使用完整数据集（每个数据集分别为 160 和 240 个卷积）相当的分割结果，而相对于训练更多的体积，训练更多的切片注释提供更有价值的信息。在切片注释的评估中，中央区域注释的效果最佳，而顶部区域注释的效果最差。在注释体积与切片之间的权衡中，尽可能注释更多的切片而不是更多的体积是更好的策略。