Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints

Li, Ruikun; Chen, Huai; Gong, Guanzhong; Wang, Lisheng

doi:10.1109/embc44109.2020.9176112

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…They obtained an accuracy of 0.98, 0.84, and 0.69 for the inner wall, outer wall, and tumor region segmentation, respectively [ 54 ]. Li et al [ 55 ] proposed an automatic segmentation method on 1092 MRI images, showing that the DL U-Net method can show high accuracy results with a DSC of 85.48%. Niazi et al [ 56 ] proposed a multi-class image segmentation method to discriminate between bladder layers in an automatic fashion (U-Net) for T1 histopathologically confirmed tumors and identified that a 12-layer model on hematoxylin-eosin stained images, achieved an accuracy of 89.3% ± 0.6 out of 100% for segmentation.…”

Section: Resultsmentioning

confidence: 99%

“…To overcome these issues, designing automated capabilities to segment the bladder in line with results obtained by the involvement of experts is pivotal to improving BCa management. Several studies evaluated the performance of different DL methods applied to CT-urography, MRI, hematoxylin-eosin stained, and cystoscopy images for proper bladder segmentation, and found positive results [ 53 , 54 , 55 , 56 , 57 ], yet still far for application into clinical practice.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Ferro

Falagario

Barone³

et al. 2023

Diagnostics

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Artificial intelligence is highly regarded as the most promising future technology that will have a great impact on healthcare across all specialties. Its subsets, machine learning, deep learning, and artificial neural networks, are able to automatically learn from massive amounts of data and can improve the prediction algorithms to enhance their performance. This area is still under development, but the latest evidence shows great potential in the diagnosis, prognosis, and treatment of urological diseases, including bladder cancer, which are currently using old prediction tools and historical nomograms. This review focuses on highly significant and comprehensive literature evidence of artificial intelligence in the management of bladder cancer and investigates the near introduction in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Ferro

Falagario

Barone³

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…The preprocessed image proceeds to the segmentation step. The segmentation step aims to identify and isolate areas of interest within the image [24], [25], [26], [27]. This may entail classifying various tissues and strictures or locating particular areas that are important for a given disease or condition.…”

Section: A Cadx Systemsmentioning

confidence: 99%

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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Conventional computer-aided diagnosis (CADx) systems play a crucial role in assisting medical professionals with the detection of skin diseases. However, these systems often involve manual, time-consuming, and error-prone processes. Recent studies show that machine learning models have potential to improve the accuracy of CADx systems. In this work, we present research findings aimed at improving the performance of CADx systems for detecting skin diseases by applying ensemble machine learning models. The investigation encompasses the exploration of three popular classification methods: linear discriminant analysis (LDA), support vector machine (SVM), and convolutional neural network (CNN); and an ensemble model of CNN with SVM. The HAM10000 dataset from Kaggle is used to train and test all classification models. Resampling is employed to address class imbalance in the dataset. Through rigorous experiments, the results highlight the compelling efficacy of the ensemble CNNSVM model, unveiling heightened accuracy up to 92% (from CNN accuracy 85% and SVM accuracy 83%). The outcome of this work has profound implications for artificial intelligence (AI) accelerated medical domains in advancing the accuracy and efficiency of skin disease treatment.

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“…This work has experimented with good results on OD/cup, vessel, and lung segmentation in multiple modalities. Recent work by Li et al (2020b) used an autoencoder to learn low-dimensional anatomical features to constraint the segmentation results from the main U-Net stream. To reach a similar goal, Painchaud et al (2020) proposed a model with two variational autoencoders (VAEs) for cardiac segmentation.…”

Section: Multi-task Networkmentioning

confidence: 99%

Anatomy-aided deep learning for medical image segmentation: a review

et al. 2021

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Deep learning (DL) has become widely used for medical image segmentation in recent years. However, despite these advances, there are still problems for which DL-based segmentation fails. Recently, some DL approaches had a breakthrough by using anatomical information which is the crucial cue for manual segmentation. In this paper, we provide a review of anatomy-aided DL for medical image segmentation which covers systematically summarized anatomical information categories and corresponding representation methods. We address known and potentially solvable challenges in anatomy-aided DL and present a categorized methodology overview on using anatomical information with DL from over 70 papers. Finally, we discuss the strengths and limitations of the current anatomy-aided DL approaches and suggest potential future work.

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Bladder Wall Segmentation in MRI Images via Deep Learning and Anatomical Constraints

Cited by 12 publications

References 15 publications

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Artificial Intelligence in the Advanced Diagnosis of Bladder Cancer-Comprehensive Literature Review and Future Advancement

Improving CADx System Performance for Skin Disease Detection using Ensemble Machine Learning Models

Anatomy-aided deep learning for medical image segmentation: a review

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