OCT视网膜下液的自动分割:一种交叉验证的视觉转换方法

IF 4.6 Q1 OPHTHALMOLOGY
Julie Midroni HBSc , Jack Longwell HBSc, MEng(C) , Nishaant Bhambra MD , Sueellen Demian MD , Aurora Pecaku MD , Isabela Martins Melo MD , Rajeev H. Muni MD, MSc
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引用次数: 0

摘要

目的提出一种对孔源性视网膜脱离(RRD)患者个体b片视网膜下液(SRF)进行分割的算法。特别关注鲁棒性,采用五倍交叉验证方法和保留测试集。设计:回顾性、横断面研究。在这项研究中,共使用了来自45名患者98个时间点的3819张b扫描片。方法在所有扫描上对视网膜下液进行分割。在4个海量数据集上进行预训练的基本SegFormer模型,在4532片视网膜OCT液体挑战数据集(视网膜内液体、SRF和色素上皮脱离的开放数据集)的原始b扫描数据集上进一步训练。当达到足够的性能时,我们使用迁移学习在我们的内部数据集上训练模型,通过生成SRF存在/不存在的逐像素掩码来分割SRF。使用了五重交叉验证方法,并使用了额外的保留测试集。首先对所有折叠进行训练和交叉验证,然后在保留集上进行额外测试。平均(图像的平均值)和总(所有像素的总和,与图像无关)计算每个折叠的骰子系数。主要观察指标RRD手术后视网膜下液量。结果验证折叠的平均总Dice系数为0.92,平均Dice系数为0.82,中位数Dice系数为0.92。对于测试集,平均总Dice系数为0.94,平均Dice系数为0.82,中位数Dice系数为0.92。模型在hold-out集合上表现出较强的互叠一致性,标准差仅为0.03。结论SegFormer模型具有较强的SRF分割能力。这一结果经得起所有折叠的交叉验证和保留测试。该模型是在线开源的。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Segmentation of Subretinal Fluid from OCT: A Vision Transformer Approach with Cross-Validation

Purpose

We present an algorithm to segment subretinal fluid (SRF) on individual B-scan slices in patients with rhegmatogenous retinal detachment (RRD). Particular attention is paid to robustness, with a fivefold cross-validation approach and a hold-out test set.

Design

Retrospective, cross-sectional study.

Participants

A total of 3819 B-scan slices across 98 time points from 45 patients were used in this study.

Methods

Subretinal fluid was segmented on all scans. A base SegFormer model, pretrained on 4 massive data sets, was further trained on raw B-scans from the retinal OCT fluid challenge data set of 4532 slices: an open data set of intraretinal fluid, SRF, and pigment epithelium detachment. When adequate performance was reached, transfer learning was used to train the model on our in-house data set, to segment SRF by generating a pixel-wise mask of presence/absence of SRF. A fivefold cross-validation approach was used, with an additional hold-out test set. All folds were first trained and cross-validated and then additionally tested on the hold-out set. Mean (averaged across images) and total (summed across all pixels, irrespective of image) Dice coefficients were calculated for each fold.

Main Outcome Measures

Subretinal fluid volume after surgical intervention for RRD.

Results

The average total Dice coefficient across the validation folds was 0.92, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. For the test set, the average total Dice coefficient was 0.94, the average mean Dice coefficient was 0.82, and the median Dice was 0.92. The model showed strong interfold consistency on the hold-out set, with a standard deviation of only 0.03.

Conclusions

The SegFormer model for SRF segmentation demonstrates a strong ability to segment SRF. This result holds up to cross-validation and hold-out testing, across all folds. The model is available open-source online.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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来源期刊
Ophthalmology science
Ophthalmology science Ophthalmology
CiteScore
3.40
自引率
0.00%
发文量
0
审稿时长
89 days
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