Application of U-Net Based Multiparameter Magnetic Resonance Image Fusion in the Diagnosis of Prostate Cancer

Huang, Xiaoping; Zhang, Bo; Tang, Min; Miao, Qiguang; Li, Tanping; Jia, Guang

doi:10.1109/access.2021.3061078

Cited by 12 publications

(6 citation statements)

References 50 publications

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“…First, imaging examination showed that lymph node enlargement was diagnosed as stage IIICr, but postoperative pathology confirmed that lymph nodes were negative, because not all enlarged lymph nodes in imaging examination were true metastasis. Because the latest guidelines did not recommend stratified treatment for stage III C patients, synchronous radiotherapy and chemotherapy were recommended for all these patients, which would make them miss the best time of surgical treatment and delay their condition; on the other hand, radiotherapy will make women of childbearing age lose ovarian function and thus lose reproductive function [ 24 ]. Second, imaging examination failed to find lymph node metastasis, but the operation showed positive lymph nodes (stage III CP).…”

Section: Discussionmentioning

confidence: 99%

The Application Value of CT Three-Dimensional Microscope Reconstruction Technology in the Diagnosis of Cervical Cancer

Sun¹,

Wang²,

Chen³

2022

Scanning

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In order to study the application value of CT three-dimensional microscope reconstruction technology in the diagnosis of cervical cancer. In this paper, 232 patients with newly diagnosed stage IA-II A2 and some stage III C: cervical cancer (stage IB1-IIA2 of stage f go in 2009) were selected, and 204 patients with stage IB1-IIA2 of stage 2009 fig 0 were selected. The original data of DICOM were obtained by CT scanning and imported into mics10.01 software to complete lymph node reconstruction. The short diameter value > 10 mm is used as the standard to judge whether the lymph node is metastatic. Referring to the 2018fig 0 staging standard, if it indicates that the lymph node is positive, it is IIICr stage. The gold standard is the diagnosis of III CP according to the surgical and pathological results, and then the diagnostic efficiency of III C stage is evaluated. The experimental results showed that 65 cases were diagnosed as IIIC stage, and 70 cases were diagnosed as IIICp stage. There was consistency between IIICr and IIICp stage, and the kappa value was 0.340. Using CT multiphase enhanced scanning and three-dimensional reconstruction technology to diagnose cervical cancer has high detection rate and high accuracy of staging diagnosis, which is worthy of clinical application.

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Section: Discussionmentioning

confidence: 99%

The Application Value of CT Three-Dimensional Microscope Reconstruction Technology in the Diagnosis of Cervical Cancer

Sun¹,

Wang²,

Chen³

2022

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“…Huang et al 40 introduced an automated medical image fusion technique utilizing a U‐Net model for merging MR images of the prostate. This method generates a weight distribution by considering the correlation between information in image features and diverse training objectives in multifocus.…”

Section: Related Workmentioning

confidence: 99%

“…Utilizing datasets of varying sizes and characteristics, including both public datasets and private datasets, these papers emphasize achieving high diagnostic accuracy in PCa detection tasks. Further, papers 34–64 focus on segmentation accuracy, evaluating methods using metrics like DSC, Hausdorff Distance (HD), or Intersection over Union (IoU). Additionally, they compare segmentation results with existing methods, highlighting improvements in accuracy, efficiency, or robustness, while emphasizing clinical implications for treatment planning and patient outcomes.…”

Section: Related Workmentioning

confidence: 99%

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

Nayagam,

Selvathi

2024

Int J Imaging Syst Tech

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Prostate Cancer (PCa) is a prevalent global threat to male health, contributing significantly to male cancer‐related mortality. Timely detection and management are pivotal for improved outcomes, as successful cure rates are highest in the early stages. Deep learning (DL) methodologies offer a promising avenue to enhance the precision of detection, potentially reducing mortality rates. Magnetic resonance imaging (MRI) stands out as an effective tool for PCa diagnosis, providing comprehensive visuals of the prostate and adjacent tissues. This technology aids in identifying cancerous developments early on, crucial for treatment planning. Utilizing MRI for PCa detection has demonstrated increased accuracy, minimizing unnecessary biopsies, and facilitating personalized treatment decisions. Recent studies showcase the potential of DL methods in identifying and segmenting the prostate in MRI scans. These techniques not only improve diagnostic precision but also assist in treatment planning and monitoring. Incorporating DL in MRI‐based PCa diagnosis holds immense potential for enhancing efficiency and accuracy, promising better treatment outcomes. However, further research is imperative to explore these methods comprehensively, especially in larger and more diverse patient populations. This review evaluates the progress in employing DL techniques, rooted in artificial intelligence, for categorizing and outlining the prostate and lesions in MR images, underscoring the need for continued investigation and validation in varied clinical settings.

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“…Figure 5 shows that the entire process is mainly divided into three steps: fusion, segmentation, and reconstruction. Specifically, in the fusion step, the medical image fusion algorithm [51] was used to fuse T2W and ADC sequence images into one image, and convolutional neural network model training was used to extract features from T2W and ADC sequence magnetic resonance images. Then, the effective aggregation weight to fuse these representations was predicted through the network.…”

Section: Segmentation and Reconstruction Of Magnetic Resonance Images...mentioning

confidence: 99%

Improved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet

et al. 2023

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The prostate tissue structure is complex, the shape and size change is relatively large, and the surrounding anatomical structure is complex, so the task of segmenting prostate and prostate cancer is somewhat challenging. In this paper, the idea of deformable convolution is combined with the U-net algorithm widely used in medical image segmentation. By using the deformable convolution module at a specific position in the ordinary U-Net network structure, additional offsets can be added to the convolution operator and the spatial sampling position can be changed by learning the offset of the target segmentation area. The fixed receptive field of the traditional convolution operator is shifted to an adaptive receptive field that can feel the change of features, and the segmentation accuracy of the target area is improved. Experiments show that the algorithm can improve the accuracy of prostate segmentation. In this paper, the segmentation model trained with healthy prostate data is transferred to the prostate cancer data set for secondary training by simulating the way doctors read pictures. Experiments show that the segmentation effect of the lesion area is significantly improved compared with the network model trained directly with small sample prostate cancer data. The research results can provide further exploration ideas for the application of medical domain knowledge in deep learning models.

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Application of U-Net Based Multiparameter Magnetic Resonance Image Fusion in the Diagnosis of Prostate Cancer

Cited by 12 publications

References 50 publications

The Application Value of CT Three-Dimensional Microscope Reconstruction Technology in the Diagnosis of Cervical Cancer

The Application Value of CT Three-Dimensional Microscope Reconstruction Technology in the Diagnosis of Cervical Cancer

A systematic review of deep learning methods for the classification and segmentation of prostate cancer on magnetic resonance images

Improved Prostate Biparameter Magnetic Resonance Image Segmentation Based on Def-UNet

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