An intelligent system of pelvic lymph node detection

Wang, Han; Huang, Hao; Wang, Jingling; Wei, Mingtian; Zhang, Yi; Wang, Ziqiang; Zhang, Haixian

doi:10.1002/int.22452

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…In recent years, with the successful application of deep learning in the field of medical imaging, [21][22][23][24][25][26] more and more people have tried to apply deep learning to virtual reconstruction, and many results have been achieved. [27][28][29] Morais et al 27 proposed a denoising auto-encoder.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

An intelligent system for craniomaxillofacial defecting reconstruction

Xiong

Guo

et al. 2022

Int J of Intelligent Sys

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Craniomaxillofacial defects caused by congenital or acquired reasons seriously affect patients' physical and mental health. How to accurately and objectively repair the morphology of craniomaxillofacial tissues and organs through surgery is a difficult problem, and the preoperative virtual design is crucial. Traditional preoperative virtual design methods include mirror technology, statistical shape model, and deformable template. However, these methods are complex, time-consuming, and only applicable to some types of defects. Therefore, a general, intelligent, and personalized craniomaxillofacial defect virtual reconstruction system is desired. To solve this problem, a novel deep learning method, RecGAN, is proposed in this paper. RecGAN can learn the bone morphology of normal people, repair the defect intelligently based on the patient's remaining bone, and fully adapt to the special conditions of different patients. Currently, there are no open-source maxillofacial data sets available. Thus, a new maxillofacial computed tomography image data set with 500 simulated cases and 100 clinical cases is constructed to train and validate the method. The experimental results show that RecGAN can effectively restore the normal bone and tissue morphology of the patient's craniomaxillofacial defect area, solve the problem that there is no objective repair method for craniomaxillofacial defect, and achieve the

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Section: Deep Learning Methodsmentioning

confidence: 99%

An intelligent system for craniomaxillofacial defecting reconstruction

Xiong

Guo

et al. 2022

Int J of Intelligent Sys

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“…Kang et al ( 19 ) processed detailed information with a specific attention mechanism, by which we were greatly inspired. In our previous research ( 20 ), we achieved accurate abdominal node detection based on CT scanning. It is widely accepted that early detection and partition of the abdominal lymph nodes can prevent CRC.…”

Section: Introductionmentioning

confidence: 99%

An attention-based neural network model for automatic partition of abdominal lymph nodes in CT imaging

Wang,

Huang,

Wang

et al. 2023

Quant Imaging Med Surg

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Background Abdominal lymph node partition is highly relevant to colorectal cancer (CRC) metastasis, which may further affect patient prognosis and survival quality. In the traditional diagnostic process, medical radiologists must partition all lymph nodes from the computed tomography (CT) images for further diagnostics. The manual interpretation of abdominal nodes is experience-dependent and time-consuming, especially for node partition. Therefore, automated partition methods are desirable to make the diagnostic process more accessible. Automatic abdominal lymph node partition is a challenging task due to the subtle morphological features of the nodes and the complex relative position information of the abdominal structure. Methods In this paper, a node-oriented dataset containing 6,880 nodes with partition labels was constructed by seasoned professionals through 2-round annotation due to there being no dataset with node-oriented labels to perform the partition task. In addition, specific masking strategies and attention mechanisms were proposed for the primary deep neural networks (DNNs). The specific masking strategy could utilize the positional and morphological information more substantially, which intensively exploits prior knowledge and hones the relative positional information in the lower abdomen. The comprehensive attention mechanism could introduce direction-aware information to enhance the inter-channel relationship of features and capture rich contextual relationships with multi-scale kernels. Results The experiments were based on the node-oriented dataset. The proposed method achieved superior performance [accuracy (ACC): 89.74%; F1 score (F1): 85.95%; area under the curve (AUC): 88.23%], which is significantly higher than the baseline model with several masking strategies (ACC: 62.05–86.16%; F1: 51.77–80.86%; AUC: 60.44–83.94%). For exploration of attention, the proposed method also outperformed the state-of-the-art convolutional block attention module (CBAM; ACC: 88.90%; F1: 84.09%; AUC: 86.86%) with the same proposed input form. Conclusions Experimental results indicate that the innovative method performs better in experimental metrics than other prevalent methods. The proposed method is expected to be introduced in future medical scenarios, which will help doctors to optimize the diagnosis workflow and improve partition sensitivity.

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From single to universal: tiny lesion detection in medical imaging

Zhang,

Mao,

et al. 2024

Artif Intell Rev

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Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous deep learning (DL) based studies have focused on single lesions, providing highly sensitive identification and precise localization. On the other hand, some recent studies have started to concentrate on whole-body lesions, as they could provide systemic clinical support. This paper presents a single-to-universal review of DL studies on tiny lesion detection in medical imaging, with a particular emphasis on detection models and techniques, as well as the data-related aspects such as modality, dimension, and dataset. A wide range of tasks are covered, including traditional single lesion detection tasks such as lung nodules, breast masses, thyroid nodules, and diseased lymph nodes, as well as the emerging task of universal lesion detection. Moreover, the paper provides in-depth analysis and discussion, with the hope of inspiring future research endeavors.

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An intelligent system of pelvic lymph node detection

Cited by 11 publications

References 29 publications

An intelligent system for craniomaxillofacial defecting reconstruction

An intelligent system for craniomaxillofacial defecting reconstruction

An attention-based neural network model for automatic partition of abdominal lymph nodes in CT imaging

From single to universal: tiny lesion detection in medical imaging

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