Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images

Cai, Naxin; Chen, Houjin; Li, Yanfeng; Peng, Yahui; Li, Jiaxin

doi:10.1109/tmi.2020.3035292

Cited by 14 publications

(4 citation statements)

References 51 publications

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“…In this paper, a B-spline-based Free-Form Deformation (FFD) model [ 23 ] is chosen to perform a nonrigid transformation on the lung images to be registered. The similarity measurement function selects the CRMI algorithm proposed by Cai et al [ 24 ], which combines mutual information (MI) and correlation ratio (CR). It adds the corresponding pixel grey level mapping based on the position information, which corrects the position deformation and makes up for the defect that MI only considers grayscale information and ignores pixel space information.…”

Section: Methodsmentioning

confidence: 99%

[Retracted] Correlation‐Based Mutual Information Model for Analysis of Lung Cancer CT Image

Vadivu¹,

Gupta²,

Naveed

et al. 2022

BioMed Research International

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Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person’s chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset.

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Section: Methodsmentioning

confidence: 99%

[Retracted] Correlation‐Based Mutual Information Model for Analysis of Lung Cancer CT Image

Vadivu¹,

Gupta²,

Naveed

et al. 2022

BioMed Research International

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show abstract

“…Next, he introduced the transfer learning mechanism to use the feature extraction method learned from DCNN for information extraction [ 12 , 13 ]. Finally, the information extraction results were completed through DTCLE and e-cognitive information extraction (ECLE) [ 14 , 15 ]. The experimental results show that the overall accuracy of experimental images 1, 2, and 3 using the DTCLE method are 91.7%, 88.1%, and 88.2%, respectively, and the overall accuracy of ECLE is 90.7%, 90.5%, and 87.0%.…”

Section: Related Workmentioning

confidence: 99%

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

Pan

et al. 2022

Journal of Healthcare Engineering

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Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

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“…Qi et al (2017) proposed a novel neural network named PointNet that directly consumes point clouds, and a rigid transformation was applied before conducting segmentation and classification. Cai et al (2020) proposed a landmark-based registration framework that incorporates landmark information into group-wise registration. Landmarks were extracted using the scale invariant feature transform (SIFT) algorithm, and a multiscale local rigid matching was proposed to establish landmark correspondence for the lung.…”

Section: Introductionmentioning

confidence: 99%

A landmark-supervised registration framework for multi-phase CT images with cross-distillation

Rao,

Lyu,

Feng

et al. 2024

Phys. Med. Biol.

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Multi-phase computed tomography (CT) has become a leading modality for identifying hepatic tumors. Nevertheless, the presence of misalignment in the images of different phases poses a challenge in accurately identifying and analyzing the patient's anatomy. Conventional registration methods typically concentrate on either intensity-based features or landmark-based features in isolation, so imposing limitations on the accuracy of the registration process. In this paper, we establish a nonrigid cycle-registration network that leverages semi-supervised learning techniques, wherein a point distance term based on Euclidean distance between registered landmark points is introduced into the loss function. Additionally, a cross-distillation strategy is proposed in network training to further improve registration performance which incorporates response-based knowledge concerning the distances between feature points. We conducted experiments using multi-centered liver CT datasets to evaluate the performance of the proposed method. The results demonstrate that our method outperforms baseline methods in terms of Target Registration Error (TRE). Additionally, Dice scores of the warped tumor masks were calculated. Our method consistently achieved the highest scores among all the comparing methods. Specifically, it achieved scores of 82.9% and 82.5% in the hepatocellular carcinoma (HCC) and the intrahepatic cholangiocarcinoma (ICC) dataset, respectively. The superior registration performance indicates its potential to serve as an important tool in hepatic tumor identification and analysis.

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Adaptive Weighting Landmark-Based Group-Wise Registration on Lung DCE-MRI Images

Cited by 14 publications

References 51 publications

[Retracted] Correlation‐Based Mutual Information Model for Analysis of Lung Cancer CT Image

[Retracted] Correlation‐Based Mutual Information Model for Analysis of Lung Cancer CT Image

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction

A landmark-supervised registration framework for multi-phase CT images with cross-distillation

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