Training and validation of a deep learning U-net architecture general model for automated segmentation of inner ear from CT

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-09-12 DOI:10.1186/s41747-024-00508-3
Jonathan Lim, Aurore Abily, Douraïed Ben Salem, Loïc Gaillandre, Arnaud Attye, Julien Ognard
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Abstract

Background

The intricate three-dimensional anatomy of the inner ear presents significant challenges in diagnostic procedures and critical surgical interventions. Recent advancements in deep learning (DL), particularly convolutional neural networks (CNN), have shown promise for segmenting specific structures in medical imaging. This study aimed to train and externally validate an open-source U-net DL general model for automated segmentation of the inner ear from computed tomography (CT) scans, using quantitative and qualitative assessments.

Methods

In this multicenter study, we retrospectively collected a dataset of 271 CT scans to train an open-source U-net CNN model. An external set of 70 CT scans was used to evaluate the performance of the trained model. The model’s efficacy was quantitatively assessed using the Dice similarity coefficient (DSC) and qualitatively assessed using a 4-level Likert score. For comparative analysis, manual segmentation served as the reference standard, with assessments made on both training and validation datasets, as well as stratified analysis of normal and pathological subgroups.

Results

The optimized model yielded a mean DSC of 0.83 and achieved a Likert score of 1 in 42% of the cases, in conjunction with a significantly reduced processing time. Nevertheless, 27% of the patients received an indeterminate Likert score of 4. Overall, the mean DSCs were notably higher in the validation dataset than in the training dataset.

Conclusion

This study supports the external validation of an open-source U-net model for the automated segmentation of the inner ear from CT scans.

Relevance statement

This study optimized and assessed an open-source general deep learning model for automated segmentation of the inner ear using temporal CT scans, offering perspectives for application in clinical routine. The model weights, study datasets, and baseline model are worldwide accessible.

Key Points

  • A general open-source deep learning model was trained for CT automated inner ear segmentation.

  • The Dice similarity coefficient was 0.83 and a Likert score of 1 was attributed to 42% of automated segmentations.

  • The influence of scanning protocols on the model performances remains to be assessed.

Graphical Abstract

Abstract Image

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训练和验证用于从 CT 自动分割内耳的深度学习 U-net 架构通用模型
背景内耳错综复杂的三维解剖结构给诊断程序和关键手术干预带来了巨大挑战。深度学习(DL),尤其是卷积神经网络(CNN)的最新进展已显示出在医学成像中分割特定结构的前景。本研究旨在通过定量和定性评估,训练并从外部验证一个开源 U-net DL 通用模型,用于从计算机断层扫描(CT)扫描中自动分割内耳。方法在这项多中心研究中,我们回顾性地收集了 271 个 CT 扫描数据集,用于训练一个开源 U-net CNN 模型。外部的 70 个 CT 扫描数据集用于评估训练模型的性能。该模型的功效使用 Dice 相似性系数 (DSC) 进行定量评估,并使用 4 级 Likert 分数进行定性评估。结果优化模型的平均 DSC 值为 0.83,42% 的病例 Likert 评分达到 1 分,同时处理时间显著缩短。总体而言,验证数据集的平均 DSC 明显高于训练数据集。相关性声明本研究优化并评估了一个开源通用深度学习模型,该模型可用于利用颞部 CT 扫描自动分割内耳,为临床常规应用提供了前景。该模型的权重、研究数据集和基线模型可在全球范围内访问.Key Points针对CT自动内耳分割训练了一个通用开源深度学习模型.Dice相似性系数为0.83,42%的自动分割得到了1分的Likert评分.扫描协议对模型性能的影响仍有待评估.Graphical Abstract.
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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
期刊最新文献
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