A novel quantitative measurement method for irregular tubules in breast carcinoma

Tan, Xiao Jian; Mustafa, Nazahah; Mashor, Mohd Yusoff; Rahman, Khairul Shakir Ab

doi:10.1016/j.jestch.2021.08.008

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Abdelhafiz et al (86) studied the application of deep CNN for mass segmentation in mammograms and found increased performance in terms of accuracy. Tan et al (87) recently developed a tubule segmentation method that investigates geometrical patterns and regularity measurements in tubule and non-tubule regions. This method is based on handcrafted features and conventional segmentation techniques, which are not effective and efficient for tubule structures due to their complex, irregular shapes and orientations with weak boundaries.…”

Section: Breast Lesion Segmentationmentioning

confidence: 99%

Mammography with deep learning for breast cancer detection

Wang

2024

Front. Oncol.

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X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

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Section: Breast Lesion Segmentationmentioning

confidence: 99%

Mammography with deep learning for breast cancer detection

Wang

2024

Front. Oncol.

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“…After that, they utilized graph-cut based method to group the closely located tumor nuclei and lumens. Recently, Tan et al 20 proposed a tubule segmentation approach which investigates geometrical pattern and regularity measurement in tubule and non-tubule regions. In this approach, to segment tubule in WSIs with high accuracy, spatial angle and the number of neighborhood nuclei distributions are used as hand-crafted features.…”

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confidence: 99%

Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer

Tekin¹,

Yazıcı²,

Kusetoğulları

et al. 2023

Sci Rep

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The tubule index is a vital prognostic measure in breast cancer tumor grading and is visually evaluated by pathologists. In this paper, a computer-aided patch-based deep learning tubule segmentation framework, named Tubule-U-Net, is developed and proposed to segment tubules in Whole Slide Images (WSI) of breast cancer. Moreover, this paper presents a new tubule segmentation dataset consisting of 30820 polygonal annotated tubules in 8225 patches. The Tubule-U-Net framework first uses a patch enhancement technique such as reflection or mirror padding and then employs an asymmetric encoder-decoder semantic segmentation model. The encoder is developed in the model by various deep learning architectures such as EfficientNetB3, ResNet34, and DenseNet161, whereas the decoder is similar to U-Net. Thus, three different models are obtained, which are EfficientNetB3-U-Net, ResNet34-U-Net, and DenseNet161-U-Net. The proposed framework with three different models, U-Net, U-Net++, and Trans-U-Net segmentation methods are trained on the created dataset and tested on five different WSIs. The experimental results demonstrate that the proposed framework with the EfficientNetB3 model trained on patches obtained using the reflection padding and tested on patches with overlapping provides the best segmentation results on the test data and achieves 95.33%, 93.74%, and 90.02%, dice, recall, and specificity scores, respectively.

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A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques

Siet,

Tan,

Cheor

et al. 2024

Artif Intell Rev

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Breast cancer, the earliest documented cancer in history, stands as a foremost cause of mortality, accounting for 684,996 deaths globally in 2020 (15.5% of all female cancer cases). Irrespective of socioeconomic factors, geographic locations, race, or ethnicity, breast cancer ranks as the most frequently diagnosed cancer in women. The standard grading for breast cancer utilizes the Nottingham Histopathology Grading (NHG) system, which considers three crucial features: mitotic counts, nuclear pleomorphism, and tubule formation. Comprehensive reviews on features, for example, mitotic count and nuclear pleomorphism have been available thus far. Nevertheless, a thorough investigation specifically focusing on tubule formation aligned with the NHG system is currently lacking. Motivated by this gap, the present study aims to unravel tubule formation in histopathology images via a comprehensive review of detection approaches involving tubule and tumor features. Without temporal constraints, a structured methodology is established in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, resulting in 12 articles for tubule detection and 67 included articles for tumor detection. Despite the primary focus on breast cancer, the structured search string extends beyond this domain to encompass any cancer type utilizing histopathology images as input, focusing on tubule and tumor detection. This broadened scope is essential. Insights from approaches in tubule and tumor detection for various cancers can be assimilated, integrated, and contributed to an enhanced understanding of tubule formation in breast histopathology images. This study compiles evidence-based analyses into a cohesive document, offering comprehensive information to a diverse audience, including newcomers, experienced researchers, and stakeholders interested in the subject matter.

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A novel quantitative measurement method for irregular tubules in breast carcinoma

Cited by 5 publications

References 23 publications

Mammography with deep learning for breast cancer detection

Mammography with deep learning for breast cancer detection

Tubule-U-Net: a novel dataset and deep learning-based tubule segmentation framework in whole slide images of breast cancer

A comprehensive review of tubule formation in histopathology images: advancement in tubule and tumor detection techniques

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