Multiparametric magnetic resonance imaging (mpMRI) is a key tool for
assessing breast cancer progression. Although deep learning has been applied to
automate tumor segmentation in breast MRI, the effect of sequence combinations
in mpMRI remains under-investigated. This study explores the impact of
different combinations of T2-weighted (T2w), dynamic contrast-enhanced MRI
(DCE-MRI) and diffusion-weighted imaging (DWI) with apparent diffusion
coefficient (ADC) map on breast tumor segmentation using nnU-Net. Evaluated on
a multicenter mpMRI dataset, the nnU-Net model using DCE sequences achieved a
Dice similarity coefficient (DSC) of 0.69 $\pm$ 0.18 for functional tumor
volume (FTV) segmentation. For whole tumor mask (WTM) segmentation, adding the
predicted FTV to DWI and ADC map improved the DSC from 0.57 $\pm$ 0.24 to 0.60
$\pm$ 0.21. Adding T2w did not yield significant improvement, which still
requires further investigation under a more standardized imaging protocol. This
study serves as a foundation for future work on predicting breast cancer
treatment response using mpMRI.

使用 nnU-Net 模型，研究探讨不同序列的 mpMRI 在乳腺肿瘤分割中的影响，并通过在多中心 mpMRI 数据集上的评估，证明了使用动态对比增强 MRI 序列可以获得较高的功能性肿瘤体积分割 Dice 相似系数和整个肿瘤掩膜分割改善。然而，添加 T2w 序列对结果影响不显著，需要进一步研究。该研究为未来的乳腺癌治疗反应预测提供基础。

评估序列组合对多参数磁共振成像乳腺肿瘤分割的影响

Evaluating the Impact of Sequence Combinations on Breast Tumor  Segmentation in Multiparametric MRI

Multiparametric magnetic resonance imaging (mpMRI) has demonstrated promising
results in prostate cancer (PCa) detection using deep convolutional neural
networks (CNNs). Recently, transformers have achieved competitive performance
compared to CNNs in computer vision. Large-scale transformers need abundant
annotated data for training, which are difficult to obtain in medical imaging.
Self-supervised learning can effectively leverage unlabeled data to extract
useful semantic representations without annotation and its associated costs.
This can improve model performance on downstream tasks with limited labelled
data and increase generalizability. We introduce a novel end-to-end
Cross-Shaped windows (CSwin) transformer UNet model, CSwin UNet, to detect
clinically significant prostate cancer (csPCa) in prostate bi-parametric MR
imaging (bpMRI) and demonstrate the effectiveness of our proposed
self-supervised pre-training framework. Using a large prostate bpMRI dataset
with 1500 patients, we first pre-train CSwin transformer using multi-task
self-supervised learning to improve data-efficiency and network
generalizability. We then finetuned using lesion annotations to perform csPCa
detection. Five-fold cross validation shows that self-supervised CSwin UNet
achieves 0.888 AUC and 0.545 Average Precision (AP), significantly
outperforming four state-of-the-art models (Swin UNETR, DynUNet, Attention
UNet, UNet). Using a separate bpMRI dataset with 158 patients, we evaluated our
model robustness to external hold-out data. Self-supervised CSwin UNet achieves
0.79 AUC and 0.45 AP, still outperforming all other comparable methods and
demonstrating generalization to a dataset shift.

本研究通过引入自监督预训练框架，提出了一种全新的端到端 Cross-Shaped windows (CSwin) transformer UNet 模型，用于检测临床重要的前列腺癌症，通过在大量未标记的数据上进行学习，提高数据效率和网络的泛化能力，成功地提高了模型性能。