基于深度学习分割的脑部计算机断层扫描骨质移除改进了硬膜下血肿检测。

IF 3 3区医学 Q2 CLINICAL NEUROLOGY

Journal of Neuroradiology Pub Date : 2024-11-08 DOI:10.1016/j.neurad.2024.101231

Masis Isikbay , M.Travis Caton , Jared Narvid , Jason Talbott , Soonmee Cha , Evan Calabrese

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引用次数: 0

摘要

目的：及时识别颅内血制品对临床具有重要影响，但由于受到邻近颅骨的干扰，在头部非对比 CT 扫描（NCCTH）上检测硬膜下血肿（SDH）具有挑战性。这项工作探索了一种 NCCTH 骨去除算法在改善 SDH 检测方面的实用性：方法：设计/训练了一种深度学习分割算法，用于使用 100 个 NCCTH 去除骨骼。使用自动分割和专家手动分割之间的定量重叠分析，对来自同一机构的 15 个 NCCTH 和来自独立外部数据集的 22 个 NCCTH 的分割准确性进行了评估。通过一项读者研究，比较了去骨和未去骨匹配病例的检测性能，评估了去骨对放射科初级学员检测 SDH 的影响：结果：来自内部和外部测试数据集的自动分割和人工分割的平均 Dice 重叠率分别为 0.9999 和 0.9957，优于其他公开可用的方法。在受训读者中，与单独使用标准 NCCTH 相比，使用去除和不去除骨骼的 NCCTH 在统计学上提高了 SDH 检测率（P 值结论）：基于深度学习分割的 NCCTH 骨去除快速、准确，与标准 NCCTH 结合使用可提高放射科实习医生对 SDH 的检测率。这项研究强调了骨切除在提高 SDH 检测的可信度和准确性方面的潜力。

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Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection

Purpose

Timely identification of intracranial blood products is clinically impactful, however the detection of subdural hematoma (SDH) on non-contrast CT scans of the head (NCCTH) is challenging given interference from the adjacent calvarium. This work explores the utility of a NCCTH bone removal algorithm for improving SDH detection.

Methods

A deep learning segmentation algorithm was designed/trained for bone removal using 100 NCCTH. Segmentation accuracy was evaluated on 15 NCCTH from the same institution and 22 NCCTH from an independent external dataset using quantitative overlap analysis between automated and expert manual segmentations. The impact of bone removal on detecting SDH by junior radiology trainees was evaluated with a reader study comparing detection performance between matched cases with and without bone removal applied.

Results

Average Dice overlap between automated and manual segmentations from the internal and external test datasets were 0.9999 and 0.9957, which was superior to other publicly available methods. Among trainee readers, SDH detection was statistically improved using NCCTH with and without bone removal applied compared to standard NCCTH alone (P value <0.001). Additionally, 12/14 (86 %) of participating trainees self-reported improved detection of extra axial blood products with bone removal, and 13/14 (93 %) indicated that they would like to have access to NCCTH bone removal in the on-call setting.

Conclusion

Deep learning segmentation-based NCCTH bone removal is rapid, accurate, and improves detection of SDH among trainee radiologists when used in combination with standard NCCTH. This study highlights the potential of bone removal for improving confidence and accuracy of SDH detection.

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来源期刊

Journal of Neuroradiology 医学-核医学

CiteScore

6.10

自引率

5.70%

发文量

142

审稿时长

6-12 weeks

期刊介绍： The Journal of Neuroradiology is a peer-reviewed journal, publishing worldwide clinical and basic research in the field of diagnostic and Interventional neuroradiology, translational and molecular neuroimaging, and artificial intelligence in neuroradiology. The Journal of Neuroradiology considers for publication articles, reviews, technical notes and letters to the editors (correspondence section), provided that the methodology and scientific content are of high quality, and that the results will have substantial clinical impact and/or physiological importance.