Using a 22-layer U-Net to perform segmentation of squamous cell carcinoma on digitized head and neck histological images

Mavuduru, Amol; Halicek, Martin; Shahedi, Maysam; Little, James V.; Chen, Amy Y.; Myers, Larry L.; Fei, Baowei

doi:10.1117/12.2549061

Cited by 9 publications

(3 citation statements)

References 9 publications

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“… 24 Mavuduru et al evaluated the ability of a CNN using U-Net tested on 200 tissue samples from 84 HNSCC patients from a single center. Their study showed an AUC of the testing group of 0.89, with a threshold of 0.2845 and average time of segmentation from WSI to be 72 s. 25 In the study of Rodner et al, a CNN was used to diagnose HNSCC distinguishing between cancer, normal epithelium, background stroma, and other tissue types on 114 images from 12 patients. Average and overall recognition rates for the 4 classes were 88.9% and 86.7%, respectively.…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence in head and neck cancer diagnosis

Bassani

Nicola

Eccher³

et al. 2022

Journal of Pathology Informatics

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence in head and neck cancer diagnosis

Bassani

Nicola

Eccher³

et al. 2022

Journal of Pathology Informatics

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“…Popular CNNs like UNet and Inceptionv4 have shown significant promise in identifying and localising regions of abnormality on digital slides for oral squamous cell carcinoma (34; 35). With the aid of DL methods, Nanditha et al (36) developed an automated system to examine oral lesions.…”

Section: Related Workmentioning

confidence: 99%

Supremacy of attention based convolution neural network in classification of oral cancer using histopathological images

Deo¹,

Pal

Panigrahi

et al. 2022

Preprint

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Introduction: Oral cancer has grown to be one of the most prevalent malignant tumours and one of the deadliest diseases in emerging and low-to-middle income nations. The mortality rate can be significantly reduced if oral cancer is detected early and treated effectively. Objectives: This study proposes an effective histopathological image classification model for oral cancer diagnosis using Vision Transformer deep learning based on multi-head attention mechanism. Methods: The oral histopathological image dataset used in the study consists of 4946 images, which were categorized into 2435 images of healthy oral mucosa and 2511 images of oral squamous cell carcinoma (OSCC). In our proposed approach, along with Vision Transformer model eight pre-trained deep learning models known as Xception, Resnet50, InceptionV3, InceptionResnetV2, Densenet121, Densenet169, Densenet201 and EfficientNetB7 have been used for the comparative analysis. 90% of the images are used for training the models while the rest 10% of the images are used for testing purposes. Results: Vision Transformer model achieved the highest classification accuracy of 97.78% in comparison to other considered deep learning models. Specificity, sensitivity and ROC AUC score are recorded as 96.88%, 98.74% and 97.74% respectively. Conclusion: We found that our proposed Vision Transformer model outperforms compared to other pre-trained deep learning models, demonstrating a stronger transfer ability of the learning in histopathological image classification from the analysis of the obtained results. This method considerably lowers the cost of diagnostic testing while increasing the diagnostic effectiveness, and accuracy for oral cancer detection in patients of diverse origin.

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“…Head and neck squamous cell carcinoma (HNSCC), the seventh most common cancer globally, accounts for 890 000 new cases and 450 000 deaths annually (Barsouk et al 2023). It is often curable if diagnosed at an early stage with a 5 year overall survival rate over 90% and below 50% for late stage diagnosis (Mavuduru et al 2020, FH et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Philip,

Watts,

Moeini

et al. 2024

Phys. Med. Biol.

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Objective: Accurate and reproducible tumor delineation on Positron Emission Tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in Head and Neck Squamous Cell Carcinoma (HNSCC) PET images.Approach: We employed manual and six semi-automatic segmentation methods (Just Enough Interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual method versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics ¬- the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA) - to evaluate the agreement between the manual and semi-automatic methods.Main Results: Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91.Significance: This study demonstrated that the JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

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Using a 22-layer U-Net to perform segmentation of squamous cell carcinoma on digitized head and neck histological images

Cited by 9 publications

References 9 publications

Artificial intelligence in head and neck cancer diagnosis

Artificial intelligence in head and neck cancer diagnosis

Supremacy of attention based convolution neural network in classification of oral cancer using histopathological images

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

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