Evolutionarily Optimized Electromagnetic Sensor Measurements for Robust Surgical Navigation

Luo, Xiangfeng; Shi, Chaoyang; Zeng, Huiqing; Ewurum, Henry Chidozie; Wan, Ying; Guo, Yinbiao; Pagnha, Seang; Zhang, Xiaobin; Du, Yanping; He, Xiangjian

doi:10.1109/jsen.2019.2928835

Cited by 12 publications

(1 citation statement)

References 27 publications

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“…Sensors and sensing systems play important roles in various medical applications, including disease diagnosis, monitoring, preoperative planning, surgical navigation, and so on [ 1 , 2 , 3 , 4 ]. Registration is one of the fundamental technologies that enable the sensing systems to be used in the above-mentioned applications [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images

Yang

Zhao

Liao

et al. 2021

Sensors

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Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.

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