Segment anything with inception module for automated segmentation of endometrium in ultrasound images.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Journal of Medical Imaging Pub Date : 2024-05-01 Epub Date: 2024-05-30 DOI:10.1117/1.JMI.11.3.034504
Yang Qiu, Zhun Xie, Yingchun Jiang, Jianguo Ma
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引用次数: 0

Abstract

Purpose: Accurate segmentation of the endometrium in ultrasound images is essential for gynecological diagnostics and treatment planning. Manual segmentation methods are time-consuming and subjective, prompting the exploration of automated solutions. We introduce "segment anything with inception module" (SAIM), a specialized adaptation of the segment anything model, tailored specifically for the segmentation of endometrium structures in ultrasound images.

Approach: SAIM incorporates enhancements to the image encoder structure and integrates point prompts to guide the segmentation process. We utilized ultrasound images from patients undergoing hysteroscopic surgery in the gynecological department to train and evaluate the model.

Results: Our study demonstrates SAIM's superior segmentation performance through quantitative and qualitative evaluations, surpassing existing automated methods. SAIM achieves a dice similarity coefficient of 76.31% and an intersection over union score of 63.71%, outperforming traditional task-specific deep learning models and other SAM-based foundation models.

Conclusions: The proposed SAIM achieves high segmentation accuracy, providing high diagnostic precision and efficiency. Furthermore, it is potentially an efficient tool for junior medical professionals in education and diagnosis.

利用初始模块对超声图像中的子宫内膜进行自动分割。
目的:准确分割超声图像中的子宫内膜对妇科诊断和治疗计划至关重要。手动分割方法既费时又主观,因此需要探索自动化解决方案。我们引入了 "segment anything with inception module"(SAIM),它是对segment anything 模型的专门调整,专门用于分割超声图像中的子宫内膜结构:方法:SAIM 对图像编码器结构进行了改进,并集成了点提示功能,以指导分割过程。我们利用在妇科接受宫腔镜手术的患者的超声图像来训练和评估该模型:我们的研究通过定量和定性评估证明了 SAIM 优越的分割性能,超越了现有的自动方法。SAIM的骰子相似系数达到76.31%,交集大于联合得分达到63.71%,优于传统的特定任务深度学习模型和其他基于SAM的基础模型:所提出的 SAIM 实现了较高的分割准确性,提供了较高的诊断精度和效率。此外,它还是初级医疗专业人员进行教育和诊断的有效工具。
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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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