基于深度学习和任意分割模型(SAM)的黑血MRI脑转移瘤检测和分割

IF 2.8 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Sang Kyun Yoo, Tae Hyung Kim, Jin Sung Kim, Sung Soo Ahn, Eui Hyun Kim, Wonmo Sung, Hojin Kim, Hong In Yoon
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引用次数: 0

摘要

目的:黑血(BB)磁共振成像(MRI)为脑转移瘤(BMs)的检测和分割提供了优越的图像对比度。本研究探讨了深度学习(DL)架构和后处理在BB图像的脑转移检测和分割中的有效性和准确性。材料与方法:收集50例患者的BB图像,对DL模型进行训练(40)和测试(10)。为了确保一致性,我们实现了分段线性直方图匹配,用于强度归一化和重采样。将改进的U-Net与生成对抗网络(GAN)相结合,提高了分割性能。基于u - net的网络生成了表示感兴趣区域的边界框,然后使用分段任意模型(SAM)在后处理中对其进行处理。我们定量评估了三种基于u - net的模型及其后处理模型的病变敏感性(LWS)、患者骰子相似系数(DSC)和平均假阳性率(FPR)。结果:GAN改良U-Net的患者DSC为0.853,LWS为89.19%,优于标准U-Net(患者DSC为0.815)和改良U-Net(患者DSC为0.846)。将GAN结构与改进的U-Net相结合也降低了FPR,平均小于1。在基于u - net的模型中,SAM的后处理对LWS和FPR没有影响,但有效地提高了患者的DSC,提高了2%-3%。结论:对标准U-Net的改进显著提高了BB图像中脑转移的检测和分割效果,采用SAM进行后处理可进一步提高分割结果的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).

Purpose: Black-blood (BB) magnetic resonance images (MRI) offer superior image contrast for the detection and segmentation of brain metastases (BMs). This study investigated the efficacy and accuracy of deep learning (DL) architectures and post-processing for BMs detection and segmentation with BB images.

Materials and methods: The BB images of 50 patients were collect to train (40) and test (10) the DL model. To ensure consistency, we implemented piecewise linear histogram matching for intensity normalization and resampling. Modified U-Net, including combination with generative adversarial network (GAN), was applied to enhance the segmentation performance. The U-Net-based networks generated bounding boxes indicating regions of interest, which were then processed in a post-processing using the Segment Anything Model (SAM). We quantitatively assessed the three U-Net-based models and their post-processed counterparts in terms of lesion-wise sensitivity (LWS), patient-wise dice similarity coefficient (DSC), and average false-positive rate (FPR).

Results: The modified U-Net with GAN yielded a patient-wise DSC of 0.853 and a LWS of 89.19%, which outperformed the standard U-Net (patient-wise DSC of 0.815) and modified U-Net only (patient-wise DSC of 0.846). Combining GAN architecture with modified U-Net also reduced the FPR, less than 1 on average. Post-processing with SAM further did not affect LWS and FPR, but effectively enhanced the patient-wise DSC by 2%-3% for the U-Net-based models.

Conclusion: The modifications to standard U-Net notably improves the detection and segmentation of BMs in BB images, and applying SAM as post-processing can further enhance the precision of segmentation results.

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来源期刊
Yonsei Medical Journal
Yonsei Medical Journal 医学-医学:内科
CiteScore
4.50
自引率
0.00%
发文量
167
审稿时长
3 months
期刊介绍: The goal of the Yonsei Medical Journal (YMJ) is to publish high quality manuscripts dedicated to clinical or basic research. Any authors affiliated with an accredited biomedical institution may submit manuscripts of original articles, review articles, case reports, brief communications, and letters to the Editor.
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