Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model

Turk, F. J.; Lüy, Murat; Barışçı, Necaattin

doi:10.3390/math8101772

Cited by 62 publications

(33 citation statements)

References 35 publications

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“…The developed model was tested on the KiTS19 dataset for kidney and kidney tumor segmentation. The Dice coefficient was found to be 86.5% for tumor segmentation [20].…”

Section: Related Workmentioning

confidence: 95%

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Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

Turk¹,

Lüy²,

Barışçı³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Cases of kidney cancer have shown a rapid increase in recent years. Advanced technology has allowed bettering the existing treatment methods. Research on the subject is still continuing. Medical segmentation is also of increasing importance. In particular, deep learning-based studies are of great importance for accurate segmentation. Tumor detection is a relatively difficult procedure for soft tissue organs such as kidneys and the prostate. Kidney tumors, specifically, are a type of cancer with a higher incidence in older people. As age progresses, the importance of having diagnostic tests increases. In some cases, patients with kidney tumors may not show any serious symptoms until the last stage. Therefore, early diagnosis of the tumor is important. This study aimed to develop support systems that could help physicians in the segmentation of kidney tumors. In the study, improvements were made on the encoder and decoder phases of the V-Net model. With the double-stage bottleneck block structure, the architecture was transformed into a unique one, which achieved an 86.9% kidney tumor Dice similarity coefficient. The results show that the model gives applicable and accurate results for kidney tumor segmentation.

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“…The developed model was tested on the KiTS19 dataset for kidney and kidney tumor segmentation. The Dice coefficient was found to be 86.5% for tumor segmentation [20].…”

Section: Related Workmentioning

confidence: 95%

“…Each upsampling block contains an upsampling layer, two convolution layers, a ReLU layer, and a bulk normalization layer. The ResNet++ architecture has been added to the last codec block [20].…”

Section: Decoder Phasementioning

confidence: 99%

Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

Turk¹,

Lüy²,

Barışçı³

et al. 2022

Intelligent Automation &Amp; Soft Computing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, advancements in deep learning (DL) have led to increased use of DL algorithms [34], particularly stacked sparse autoencoders (SSAEs) for automated medical image segmentation, classification [35], and detection [36][37][38][39][40][41]. The deep-stacked autoencoder (SAE) framework of deep learning was used for liver segmentation in [42].…”

Section: Related Workmentioning

confidence: 99%

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

et al. 2022

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Precise vertebrae segmentation is essential for the image-related analysis of spine pathologies such as vertebral compression fractures and other abnormalities, as well as for clinical diagnostic treatment and surgical planning. An automatic and objective system for vertebra segmentation is required, but its development is likely to run into difficulties such as low segmentation accuracy and the requirement of prior knowledge or human intervention. Recently, vertebral segmentation methods have focused on deep learning-based techniques. To mitigate the challenges involved, we propose deep learning primitives and stacked Sparse autoencoder-based patch classification modeling for Vertebrae segmentation (SVseg) from Computed Tomography (CT) images. After data preprocessing, we extract overlapping patches from CT images as input to train the model. The stacked sparse autoencoder learns high-level features from unlabeled image patches in an unsupervised way. Furthermore, we employ supervised learning to refine the feature representation to improve the discriminability of learned features. These high-level features are fed into a logistic regression classifier to fine-tune the model. A sigmoid classifier is added to the network to discriminate the vertebrae patches from non-vertebrae patches by selecting the class with the highest probabilities. We validated our proposed SVseg model on the publicly available MICCAI Computational Spine Imaging (CSI) dataset. After configuration optimization, our proposed SVseg model achieved impressive performance, with 87.39% in Dice Similarity Coefficient (DSC), 77.60% in Jaccard Similarity Coefficient (JSC), 91.53% in precision (PRE), and 90.88% in sensitivity (SEN). The experimental results demonstrated the method’s efficiency and significant potential for diagnosing and treating clinical spinal diseases.

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“…And [ 44 ] adds the emerging attention mechanism to a nested U-Net architecture for image segmentation on liver CT scans. One interesting medical application of image segmentation using a deep learning model is presented in [ 45 ]. A new hybrid of the classic V-Net architecture is used to help detect kidney and renal tumors on CT imaging with successful performance of medical segmentation.…”

Section: Related Workmentioning

confidence: 99%

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

Rochac

Zhang

Thompson

et al. 2021

Computational Intelligence and Neuroscience

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Hyperspectral imaging is an area of active research with many applications in remote sensing, mineral exploration, and environmental monitoring. Deep learning and, in particular, convolution-based approaches are the current state-of-the-art classification models. However, in the presence of noisy hyperspectral datasets, these deep convolutional neural networks underperform. In this paper, we proposed a feature augmentation approach to increase noise resistance in imbalanced hyperspectral classification. Our method calculates context-based features, and it uses a deep convolutional neuronet (DCN). We tested our proposed approach on the Pavia datasets and compared three models, DCN, PCA + DCN, and our context-based DCN, using the original datasets and the datasets plus noise. Our experimental results show that DCN and PCA + DCN perform well on the original datasets but not on the noisy datasets. Our robust context-based DCN was able to outperform others in the presence of noise and was able to maintain a comparable classification accuracy on clean hyperspectral images.

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Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model

Cited by 62 publications

References 35 publications

Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

Kidney Tumor Segmentation Using Two-Stage Bottleneck Block Architecture

SVseg: Stacked Sparse Autoencoder-Based Patch Classification Modeling for Vertebrae Segmentation

A Robust Context‐Based Deep Learning Approach for Highly Imbalanced Hyperspectral Classification

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