Strip and boundary detection multi-task learning network for segmentation of meibomian glands

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2024-11-26 DOI:10.1002/mp.17542

Weifang Zhu, Dengfeng Liu, Xinyu Zhuang, Tian Gong, Fei Shi, Dehui Xiang, Tao Peng, Xiaofeng Zhang, Xinjian Chen

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引用次数: 0

Abstract

Background

Automatic segmentation of meibomian glands in near-infrared meibography images is basis of morphological parameter analysis, which plays a crucial role in facilitating the diagnosis of meibomian gland dysfunction (MGD). The special strip shape and the adhesion between glands make the automatic segmentation of meibomian glands very challenging.

Purpose

A strip and boundary detection multi-task learning network (SBD-MTLNet) based on encoder-decoder structure is proposed to realize the automatic segmentation of meibomian glands.

Methods

A strip mixed attention module (SMAM) is proposed to enhance the network's ability to recognize the strip shape of glands. To alleviate the problem of adhesion between glands, a boundary detection auxiliary network (BDA-Net) is proposed, which introduces boundary features to assist gland segmentation. A self-adaptive interactive information fusion module (SIIFM) based on reverse attention mechanism is proposed to realize information complementation between meibomian gland segmentation and boundary detection tasks. The proposed SBD-MTLNet has been evaluated on an in-house dataset (453 images) and a public dataset MGD-1K (1000 images). Due to the limited number of images, a five-fold cross validation strategy is adopted.

Results

Average dice coefficient of the proposed SBD-MTLNet reaches 81.08% and 84.32% on the in-house dataset and the public one, respectively. Comprehensive experimental results demonstrate the effectiveness the proposed SBD-MTLNet, outperforming other state-of-the-art methods.

Conclusions

The proposed SBD-MTLNet can focus more on the shape characteristics of the meibomian glands and the boundary contour information between the adjacent glands via multi-task learning strategy. The segmentation results of the proposed method can be used for the quantitative morphological characteristics analysis of meibomian glands, which has potential for the auxiliary diagnosis of MGD in clinic.

查看原文本刊更多论文

用于分割睑板腺的条带和边界检测多任务学习网络。

背景：近红外睑板腺造影图像中睑板腺的自动分割是形态学参数分析的基础，对促进睑板腺功能障碍（MGD）的诊断起着至关重要的作用。目的：提出一种基于编码器-解码器结构的条带和边界检测多任务学习网络（SBD-MTLNet），以实现睑板腺的自动分割：方法：提出了一种条状混合注意力模块（SMAM），以增强网络识别腺体条状形状的能力。为了缓解腺体之间的粘连问题，提出了边界检测辅助网络（BDA-Net），该网络引入边界特征来辅助腺体分割。提出了基于反向注意机制的自适应交互式信息融合模块（SIIFM），以实现睑板腺分割和边界检测任务之间的信息互补。所提出的 SBD-MTLNet 在内部数据集（453 幅图像）和公共数据集 MGD-1K （1000 幅图像）上进行了评估。由于图像数量有限，因此采用了五倍交叉验证策略：结果：拟议的 SBD-MTLNet 在内部数据集和公共数据集上的平均骰子系数分别达到了 81.08% 和 84.32%。综合实验结果证明了所提出的 SBD-MTLNet 的有效性，优于其他最先进的方法：结论：所提出的 SBD-MTLNet 通过多任务学习策略，能更多地关注睑板腺的形状特征和相邻睑板腺之间的边界轮廓信息。所提方法的分割结果可用于睑板腺形态特征的定量分析，具有临床辅助诊断睑板腺肥大症的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.