The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer

Jin, Juebin; Zhu, Haiyan; Yingyan, Teng; Ai, Yao; Xie, Congying; Jin, Xiance

doi:10.1007/s10278-022-00620-z

Cited by 12 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies using python and Pyradiomics for automatic segmentation and feature extraction demonstrated that U-net models are able to achieve a DSC around 0.88 to 0.90 in US images for cervical cancer with an average ICC around 0.99. 15 …”

Section: Discussionmentioning

confidence: 99%

“…The construction and accuracy of these automatic segmentation models for US images had been reported in previous studies. 14,15 Feature Extraction and Model Building…”

Section: Manual and Automatic Segmentationmentioning

confidence: 99%

“… 13 Previously, automatic segmentations using deep learning algorithm on US and magnetic resonance (MR) images were investigated and reported a generally high accuracy for patients with ovarian cancer and cervical cancer. 14 – 16 The purpose of this study is to further investigate the effects of these automatic segmentation algorithms on the accuracy of radiomics modeling in predicting the status of LNM for patients with early stage cervical cancer preoperatively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

Teng

Tao

et al. 2022

Technol Cancer Res Treat

Self Cite

View full text Add to dashboard Cite

Introduction: The purpose of this study is to investigate the effects of automatic segmentation algorithms on the performance of ultrasound (US) radiomics models in predicting the status of lymph node metastasis (LNM) for patients with early stage cervical cancer preoperatively. Methods: US images of 148 cervical cancer patients were collected and manually contoured by two senior radiologists. The four deep learning-based automatic segmentation models, namely U-net, context encoder network (CE-net), Resnet, and attention U-net were constructed to segment the tumor volumes automatically. Radiomics features were extracted and selected from manual and automatically segmented regions of interest (ROIs) to predict the LNM of these cervical cancer patients preoperatively. The reliability and reproducibility of radiomics features and the performances of prediction models were evaluated. Results: A total of 449 radiomics features were extracted from manual and automatic segmented ROIs with Pyradiomics. Features with an intraclass coefficient (ICC) > 0.9 were all 257 (57.2%) from manual and automatic segmented contours. The area under the curve (AUCs) of validation models with radiomics features extracted from manual, attention U-net, CE-net, Resnet, and U-net were 0.692, 0.755, 0.696, 0.689, and 0.710, respectively. Attention U-net showed best performance in the LNM prediction model with a lowest discrepancy between training and validation. The AUCs of models with automatic segmentation features from attention U-net, CE-net, Resnet, and U-net were 9.11%, 0.58%, –0.44%, and 2.61% higher than AUC of model with manual contoured features, respectively. Conclusion: The reliability and reproducibility of radiomics features, as well as the performance of radiomics models, were affected by manual segmentation and automatic segmentations.

show abstract

Section: Discussionmentioning

confidence: 99%

“…The construction and accuracy of these automatic segmentation models for US images had been reported in previous studies. 14,15 Feature Extraction and Model Building…”

Section: Manual and Automatic Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

Teng

Tao

et al. 2022

Technol Cancer Res Treat

Self Cite

View full text Add to dashboard Cite

show abstract

“…The effect of segmentation accuracy on the performance of radiomic features for different cancer sites and imaging modalities is a subject yet to be explored in the literature. Among research done so far, Jin et al studied the effect of automatic segmentation using multiple UNet-based architectures on the accuracy of radiomic features for transvaginal ultrasound images of cervical cancer [ 33 ]. The results of that study show the feasibility and reliability of automatic segmentation, especially with UNet-based models, for relevant radiomic studies.…”

Section: Discussionmentioning

confidence: 99%

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

Jalalifar

Soliman

Sahgal

et al. 2022

Cancers

View full text Add to dashboard Cite

Significantly affecting patients’ clinical course and quality of life, a growing number of cancer cases are diagnosed with brain metastasis (BM) annually. Stereotactic radiotherapy is now a major treatment option for patients with BM. However, it may take months before the local response of BM to stereotactic radiation treatment is apparent on standard follow-up imaging. While machine learning in conjunction with radiomics has shown great promise in predicting the local response of BM before or early after radiotherapy, further development and widespread application of such techniques has been hindered by their dependency on manual tumour delineation. In this study, we explored the impact of using less-accurate automatically generated segmentation masks on the efficacy of radiomic features for radiotherapy outcome prediction in BM. The findings of this study demonstrate that while the effect of tumour delineation accuracy is substantial for segmentation models with lower dice scores (dice score ≤ 0.85), radiomic features and prediction models are rather resilient to imperfections in the produced tumour masks. Specifically, the selected radiomic features (six shared features out of seven) and performance of the prediction model (accuracy of 80% versus 80%, AUC of 0.81 versus 0.78) were fairly similar for the ground-truth and automatically generated segmentation masks, with dice scores close to 0.90. The positive outcome of this work paves the way for adopting high-throughput automatically generated tumour masks for discovering diagnostic and prognostic imaging biomarkers in BM without sacrificing accuracy.

show abstract

“…Fundamentally, the bridge network is nothing but a cascade of convolution. We have fundamentally categorized the batch normalization into twelve types of variation, namely, (B1) serial cascades of convolutions [95]; (B2) convolutions with dropouts [102,134]; (B3) dropout in bridge network [80,134,138]; (B4) cascade of convolutions in serial and parallel (DAC and RMP blocks) [108,153]; (B5) bridge normalization [61,95]; (B6) flatten block [69]; (B7) atrous spatial pyramid pooling [135]; (B8) transpose convolution [137]; (B9) patch convolution and transformer [66]; (B10) inception block [97]; (B11) dense layer [114,122]; and (B12) quartent attention [82]. A set of representative examples will be discussed in section VI.…”

Section: Bridge Network and Its Variationsmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

show abstract

The Accuracy and Radiomics Feature Effects of Multiple U-net-Based Automatic Segmentation Models for Transvaginal Ultrasound Images of Cervical Cancer

Cited by 12 publications

References 36 publications

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

The Effects of Automatic Segmentations on Preoperative Lymph Node Status Prediction Models With Ultrasound Radiomics for Patients With Early Stage Cervical Cancer

Impact of Tumour Segmentation Accuracy on Efficacy of Quantitative MRI Biomarkers of Radiotherapy Outcome in Brain Metastasis

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Contact Info

Product

Resources

About