Objectives: This work aims to explore the impact of multicenter data
heterogeneity on deep learning brain metastases (BM) autosegmentation
performance, and assess the efficacy of an incremental transfer learning
technique, namely learning without forgetting (LWF), to improve model
generalizability without sharing raw data.
Materials and methods: A total of six BM datasets from University Hospital
Erlangen (UKER), University Hospital Zurich (USZ), Stanford, UCSF, NYU and
BraTS Challenge 2023 on BM segmentation were used for this evaluation. First,
the multicenter performance of a convolutional neural network (DeepMedic) for
BM autosegmentation was established for exclusive single-center training and
for training on pooled data, respectively. Subsequently bilateral collaboration
was evaluated, where a UKER pretrained model is shared to another center for
further training using transfer learning (TL) either with or without LWF.
Results: For single-center training, average F1 scores of BM detection range
from 0.625 (NYU) to 0.876 (UKER) on respective single-center test data. Mixed
multicenter training notably improves F1 scores at Stanford and NYU, with
negligible improvement at other centers. When the UKER pretrained model is
applied to USZ, LWF achieves a higher average F1 score (0.839) than naive TL
(0.570) and single-center training (0.688) on combined UKER and USZ test data.
Naive TL improves sensitivity and contouring accuracy, but compromises
precision. Conversely, LWF demonstrates commendable sensitivity, precision and
contouring accuracy. When applied to Stanford, similar performance was
observed.
Conclusion: Data heterogeneity results in varying performance in BM
autosegmentation, posing challenges to model generalizability. LWF is a
promising approach to peer-to-peer privacy-preserving model training.

多中心数据异质性对深度学习脑转移瘤自动分割性能的影响以及渐进转移学习技术（即学习无遗忘）在改善模型的推广能力方面的有效性进行了探讨。

多中心隐私保护模型训练用于深度学习脑转移自动分割

Multicenter Privacy-Preserving Model Training for Deep Learning Brain  Metastases Autosegmentation

Background: Tumor segmentation in MRI is crucial in radiotherapy (RT)
treatment planning for brain tumor patients. Segment anything (SA), a novel
promptable foundation model for autosegmentation, has shown high accuracy for
multiple segmentation tasks but was not evaluated on medical datasets yet.
Methods: SA was evaluated in a point-to-mask task for glioma brain tumor
autosegmentation on 16744 transversal slices from 369 MRI datasets (BraTS
2020). Up to 9 point prompts were placed per slice. Tumor core (enhancing tumor
+ necrotic core) was segmented on contrast-enhanced T1w sequences. Out of the 3
masks predicted by SA, accuracy was evaluated for the mask with the highest
calculated IoU (oracle mask) and with highest model predicted IoU (suggested
mask). In addition to assessing SA on whole MRI slices, SA was also evaluated
on images cropped to the tumor (max. 3D extent + 2 cm). Results: Mean best IoU
(mbIoU) using oracle mask on full MRI slices was 0.762 (IQR 0.713-0.917). Best
2D mask was achieved after a mean of 6.6 point prompts (IQR 5-9). Segmentation
accuracy was significantly better for high- compared to low-grade glioma cases
(mbIoU 0.789 vs. 0.668). Accuracy was worse using MRI slices cropped to the
tumor (mbIoU 0.759) and was much worse using suggested mask (full slices
0.572). For all experiments, accuracy was low on peripheral slices with few
tumor voxels (mbIoU, <300: 0.537 vs. >=300: 0.841). Stacking best oracle
segmentations from full axial MRI slices, mean 3D DSC for tumor core was 0.872,
which was improved to 0.919 by combining axial, sagittal and coronal masks.
Conclusions: The Segment Anything foundation model, while trained on photos,
can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRI
slices. The results suggest that Segment Anything can accelerate and facilitate
RT treatment planning, when properly integrated in a clinical application.

本文介绍了一种新的 MRI 图像中肿瘤分割算法 ——Segment Anything，该算法通过对 Glioma 脑肿瘤的满足点任务进行了评估，并成功实现高精度的肿瘤自动分割，不仅如此，该算法能够加速和便利放射治疗规划。