IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology

Mahanta, Lipi B.; Hussain, Elima; Das, Navarun; Kakoti, Lopa Mudra; Chowdhury, Manish

doi:10.1016/j.asoc.2021.107136

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…The study found that the proposed method achieved high accuracy and efficiency in quantifying DAB staining, and could be useful in the assessment of HER2 status in breast cancer diagnosis. Another study, "IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology" by [13], published in 2021 in the Journal of Applied Soft Computing, proposed a convolutional neural network-based method for quantifying DAB staining in immunohistochemistry. The study found that the proposed method achieved high accuracy and efficiency in quantifying DAB staining and could be useful in the diagnosis and treatment of various diseases.…”

Section: Discussionmentioning

confidence: 99%

Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

Fares

Khenenou

Rahmoun

2023

Preprint

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The current research aimed to establish a method for measuring the percentage of diaminobenzidine (DAB) staining on immunohistochemical slides with high accuracy and efficiency. The R programming language was utilized in this endeavor. A total of 50 slides were collected from various types of tissue, and were stained using an anti-cytokeratin antibody and the DAB detection method. These slides were then scanned using a high-resolution scanner, and the resulting images were analyzed using R, a custom script was specifically developed to segment the tissue and DAB-positive areas, and calculate the percentage of DAB staining on the slide. The results were then compared to manual measurements of DAB staining performed by a trained technician. The R-based method was found to be highly accurate, with a mean absolute error of only 0.76 % compared to manual measurements, this study provides evidence that the use of R for DAB quantification is a fast and reliable alternative to manual methods, enabling the analysis of large numbers of slides in a short period of time. It offers a valuable tool for researchers and technicians in the field of histopathology, enabling them to quickly and accurately analyze DAB staining on immunohistochemical slides, which is essential for the diagnosis and treatment of various diseases.

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Section: Discussionmentioning

confidence: 99%

Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

Fares

Khenenou

Rahmoun

2023

Preprint

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“…Mahanta et al [29] used three CNN models to extract local features, such as stained cells, stroma, and inflammation, from IHC breast cancer images and integrated the results of three classifiers to achieve a correlation of 90.0% with expert manual classifications. Kwak et al [30] used CNNs to learn advanced feature representations of nuclear structures from seed nucleus graphs to identify prostate cancer.…”

Section: A Cnnsmentioning

confidence: 99%

Cell Nucleus-Graph Convolutional Network Evaluation of Immunohistochemistry Images of Colorectal Cancer

Huang,

Mo,

yuqi

et al. 2023

Preprint

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<p>This study proposed a method for interpreting immunohistochemistry (IHC) images based on a graph convolutional network (GCN). Self-supervised transfer learning was employed to obtain cell nucleus segmentation masks, providing effective strong cues for a cell nucleus graph (CN-G). This study applys a GCN to end-to-end diagnostic classification tasks for IHC images, fully considering global distribution features and local details in images. We believe that our study makes a significant contribution to the literature because the proposed approach ensures high accuracy in the relevant tasks while addressing the challenges of the lack of labeled datasets and high number of sample pixels.</p>

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“…In recent years, automatic segmentation methods based on deep learning have developed rapidly, 3–5 and were gradually applied to the automatic segmentation of medical images. Among them, U‐net, which uniformly extracts semantic features as well as image features of targets through multiple up and down sampling operations, can efficiently and accurately segment targets with wide versatility, becoming the most widely used in the field of medical image segmentation 6–8 .…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of the tumor in nonsmall‐cell lung cancer by combining coarse and fine segmentation

et al. 2023

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Objectives: Radiotherapy plays an important role in the treatment of nonsmallcell lung cancer (NSCLC). Accurate delineation of tumor is the key to successful radiotherapy. Compared with the commonly used manual delineation ways, which are time-consuming and laborious, the automatic segmentation methods based on deep learning can greatly improve the treatment efficiency. Methods: In this paper, we introduce an automatic segmentation method by combining coarse and fine segmentations for NSCLC. Coarse segmentation network is the first level, identifing the rough region of the tumor. In this network, according to the tissue structure distribution of the thoracic cavity where tumor is located, we designed a competition method between tumors and organs at risk (OARs), which can increase the proportion of the identified tumor covering the ground truth and reduce false identification. Fine segmentation network is the second level, carrying out precise segmentation on the results of the coarse level. These two networks are independent of each other during training. When they are used, morphological processing of small scale corrosion and large scale expansion is used for the coarse segmentation results, and the outcomes are sent to the fine segmentation part as input, so as to achieve the complementary advantages of the two networks. Results: In the experiment, CT images of 200 patients with NSCLC are used to train the network, and CT images of 60 patients are used to test. Finally, our method produced the Dice similarity coefficient of 0.78 ± 0.10. Conclusions:The experimental results show that the proposed method can accurately segment the tumor with NSCLC, and can also provide support for clinical diagnosis and treatment.

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IHC-Net: A fully convolutional neural network for automated nuclear segmentation and ensemble classification for Allred scoring in breast pathology

Cited by 19 publications

References 26 publications

Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

Unlocking the Power of R: A High-Accuracy Method for Measuring DAB Staining on Immunohistochemical Slides

Cell Nucleus-Graph Convolutional Network Evaluation of Immunohistochemistry Images of Colorectal Cancer

Automatic segmentation of the tumor in nonsmall‐cell lung cancer by combining coarse and fine segmentation

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