Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images

Luo, Xiangfeng; Zeng, Wankang; Fan, Wenkang; Zheng, Shibao; Chen, Jianhui; Liu, Rong; Liu, Zengqin; Chen, Yinran

doi:10.1117/12.2581932

“…The conventional methods include thresholding and level-set segmentation [50]. The deep learning methods include single modality such as FCN [51] and dual-modality such as V-Net [35], W-Net [52] and 3D-UNet+GC [53], [54]. Besides, we compared our method with some classical segmentation approaches on the PET modality.…”

Section: B Comparison Methodsmentioning

confidence: 99%

“…To obtain high and low-level features, many researchers have introduced V-Net. [34] introduced first V-Net [35] is used to obtain CT image and the second V-Net is used to obtain pre-fused PET-CT image. [36] also used V-Net for lung tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

Xia

¹

,

Zhang

²

2023

IEEE Access

3

0

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Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic information. In this work, we have proposed a novel deep learning-based graph model network which can automatically fuse dual-modality information for tumor area segmentation. Our method rationally utilizes the advantage of each imaging modality(PET: the superior contrast, CT: the superior spatial resolution). We formulate this task as a Conditional Random Field(CRF) based on multi-scale fusion and dual-modality co-segmentation of object image with a normalization term which balances the segmentation divergence between PET and CT. This mechanism considers that the spatial varying characteristics acquire different scales, which encode various feature information over different modalities. The ability of our method was evaluated to detect and segment tumor regions with different fusion approaches using a dataset of PET/CT clinical tumor images. The results illustrated that our method effectively integrates both PET and CT modalities information, deriving segmentation accuracy result of 0.86 in DSC and the sensitivity of 0.83, which is 3.61% improvement compared to the W-Net.

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“…The conventional methods include thresholding and level-set segmentation [47]. The deep learning methods include single modality such as FCN [48] and dual-modality such as V-Net [32], W-Net [49] and 3D-UNet+GC [50,51]. Besides, we compared our method with some classical segmentation approaches on the PET modality.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…To obtain high and low-level features, many researchers have introduced V-Net. [31] introduced first V-Net [32] is used to obtain CT image and the second V-Net is used to obtain pre-fused PET-CT image. [33] also used V-Net for lung tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

Design and Validate a Dual-Modality Characteristic Information Fusion System Based on Probabilistic Graphical Models

Xia

¹

,

Zhang

²

,

Yao

³

et al. 2023

Preprint

0

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Positron Emission Tomography and Computed Tomography(PET/CT) imaging could obtain functional metabolic feature information and anatomical localization information of the patient body. However, tumor segmentation in PET/CT images is significantly challenging for fusing of dual-modality characteristic information. In this work, we have proposed a novel deep learning-based graph model network which can automatically fuse dual-modality information for tumor area segmentation. Our method rationally utilizes the advantage of each imaging modality(PET: the superior contrast, CT: the superior spatial resolution). We formulate this task as a Conditional Random Field(CRF) based on multi-scale fusion and dual-modality co-segmentation of object image with a normalization term which balances the segmentation divergence between PET and CT. This mechanism considers that the spatial varying characteristics acquire different scales, which encode various feature information over different modalities. The ability of our method was evaluated to detect and segment tumor regions with different fusion approaches using a dataset of PET/CT clinical tumor images. The results illustrated that our method effectively integrates both PET and CT modalities information, deriving segmentation accuracy result of 0.86 in DSC and the sensitivity of 0.83, which is 3.61\% improvement compared to the W-Net.

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“…Xiongbiao et al [75] conducted a study about the segmentation of kidney tumor images by using they used 210 CT images for model training and validation and 90 images for objective model evaluation; they used randomly selected 198 for training and 12 for validation from 210 training. They applied deep learning techniques such as V-net, ResNet 50 encoder with 3D convolution, and ReLU.…”

Section: Sudharson and Kokilmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

¹

,

Alzu’bi

²

,

Abdullah

³

et al. 2022

BDCC

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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

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Towards cascaded V-Net for automatic accurate kidney segmentation from abdominal CT images

Cited by 5 publications

References 0 publications

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

A Novel Lung Nodule Accurate Segmentation of PET-CT Images Based on Convolutional Neural Network and Graph Model

Design and Validate a Dual-Modality Characteristic Information Fusion System Based on Probabilistic Graphical Models

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

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