Computer methods and programs in biomedicine · 2025
Standard plane localization using denoising diffusion model with multi-scale guidance.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Dou Haoran, Huang Yuhao, Huang Yunzhi, Yang Xin, Zhen Chaojiong, Zhang Yuanji, Xiong Yi, Huang Weijun, Ni Dong
Аннотация
BACKGROUND AND OBJECTIVE: Standard planes (SPs) acquisition is a fundamental yet crucial step in routine ultrasound (US) examinations. Compared to the 2D US, 3D US offers the advantage of capturing multiple SPs in a single scan, and visualizing particular SPs (e.g., the coronal plane of the uterus). However, SPs localization in 3D US is challenging due to the vast 3D search space, anatomical variability, and poor image quality.
METHODS: In this study, we present a probabilistic method based on the conditional denoising diffusion model for SPs localization in 3D US. Specifically, we construct multi-scale guidance to provide the model with both global and local context. We improve the model's angular sensitivity by modifying the tangent-based plane representation with the spherical coordinates. We also reveal the potential in simultaneously localizing SPs and detecting their abnormality without introducing extra parameters.
RESULTS: Extensive validations were performed on a large in-house dataset containing 837 patients across two organs with four SPs. The proposed method achieved average errors of less than 10° and 1 mm in terms of the angle and distance on the four investigated SPs. Furthermore, it can obtain over 90% accuracy for detecting anomalies by simply thresholding the quantified uncertainty.
CONCLUSIONS: The results show that our proposed method significantly outperformed the current state-of-the-art approaches regarding spatial and content metrics across four SPs in two organs, indicating its superiority and generalizability. Meanwhile, the investigated anomaly detection of our method demonstrates its potential in applying clinical practice.
METHODS: In this study, we present a probabilistic method based on the conditional denoising diffusion model for SPs localization in 3D US. Specifically, we construct multi-scale guidance to provide the model with both global and local context. We improve the model's angular sensitivity by modifying the tangent-based plane representation with the spherical coordinates. We also reveal the potential in simultaneously localizing SPs and detecting their abnormality without introducing extra parameters.
RESULTS: Extensive validations were performed on a large in-house dataset containing 837 patients across two organs with four SPs. The proposed method achieved average errors of less than 10° and 1 mm in terms of the angle and distance on the four investigated SPs. Furthermore, it can obtain over 90% accuracy for detecting anomalies by simply thresholding the quantified uncertainty.
CONCLUSIONS: The results show that our proposed method significantly outperformed the current state-of-the-art approaches regarding spatial and content metrics across four SPs in two organs, indicating its superiority and generalizability. Meanwhile, the investigated anomaly detection of our method demonstrates its potential in applying clinical practice.