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

Abstract

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.

Mammography with deep learning for breast cancer detection

“…Table 3 provides a comparison of recently developed DL methods in mammography for breast lesion segmentation. These methods include the Conditional Random Field model (CRF) ( 59 ), Adversarial Deep Structured Net ( 60 ), Deep Learning using You-Only-Look-Once ( 61 ), Conditional Residual U-Net (CRU-Net) ( 62 ), Mixed-Supervision-Guided (MS-ResCU-Net) and Residual-Aided Classification U-Net Model (ResCU-Net) ( 63 ), Dense U-Net with Attention Gates (AGs) ( 64 ), Residual Attention U-Net Model (RU-Net) ( 65 ), Modified U-Net ( 66 ), Mask RCNN ( 67 ), Full-Resolution Convolutional Network (FrCN) ( 68 ), U-Net ( 69 ), Conditional Generative Adversarial Networks (cGAN) ( 70 , 71 ), DeepLab ( 72 ), Attention-Guided Dense-Upsampling Network (AUNet) ( 73 ), FPN ( 74 ), modified CNN based on U-Net Model ( 76 ), deeply supervised U-Net ( 77 ), modified U-Net ( 78 ), and Tubule-U-Net ( 79 ). Among these DL methods, U-Net is the most commonly employed segmentation method.…”

Whole slide images in artificial intelligence applications in digital pathology: challenges and pitfalls

“…Despite the growing number of digital pathology and AI studies in our country, this progress is restricted by the digitization of pathology slides and AI-oriented technical facilities (16). Although a small number of articles (17) with focus on pathology and pathologists' participation are included in national publication directories, many Turkish researchers are involved in international studies and publications (8)(9)(10)12,(18)(19)(20), and the number of publications from Turkey has been increasing (21)(22)(23)(24)(25)(26).…”

PLA—A Privacy-Embedded Lightweight and Efficient Automated Breast Cancer Accurate Diagnosis Framework for the Internet of Medical Things

“…In recent years, there have been considerable research studies focusing on utilizing deep learning techniques for the automatic classification of breast cancer based on its pathology [3][4][5][6][7][8][9][10][11][12][13][14]. Nonetheless, medical institutions, being the proprietors of image data, have exhibited a preference for training models using their data due to the stringent privacy and security regulations surrounding medical data [1].…”