Breast cancer tissue stained with ER.
In immunohistochemistry (IHC), estrogen receptor (ER) is an important biomarker in the investigation of breast cancer . This APP is developed for use in discovery of medical knowledge related to ER status in breast cancer.
In order to assess the ER status in breast cancer automatically, robust nuclei detection and segmentation is important. This APP utilizes artificial intelligence (AI) for automatic and robust nuclei detection and segmentation in ER stained breast cancer tissue.
The nuclei detection performance has been assessed using precision and recall. Precision measures the ability of the APP to only detect viable nuclei and not mistake e.g. artefacts for nuclei. Recall (also known as sensitivity) measures the ability of the APP to detect all nuclei present. Precision and recall are combined to a F1-score which reflects both in a harmonic mean. Testing on 1024 manually detected nuclei stained for ER, all three performance measures show excellent nuclei detection as seen from the table below.
The nuclei segmentation performance has been assessed using aggregated Jaccard index (AJI)  which captures the segmentation quality. Testing on 1024 manually segmented nuclei, the performance measure shows excellent nuclei segmentation as seen from the table below.
|Mean||94.4 %||96.1 %||0.952||0.811|
After segmentation of the nuclei, the APP is configured to divide them into ER negative, 1+, 2+, or 3+ positive based on the DAB intensity. The user can adjust the threshold for differentiation between the nuclei to fit their research.
The assessment of ER status by the APP has shown good correlation with manual assessment as seen from the figure below. A total of 208 ER breast cancer samples have been used for the comparison.
In conclusion, the APP offers a digital solution for automatic determination of ER status in breast cancer research.
Breast cancer tissue stained with ER.
Outlining of tumor regions in breast cancer tissue stained with ER. Outlining can be done manually, or it can be done automatically using APP “10162 – IHC, Tumor Detection, AI”, as in this case, or using our virtual double staining technique.
Result of applying APP “10171 – ER, Breast Cancer, AI” to the outlined tumor regions. Automatic nuclei segmentation is achieved using AI and classification into negative, 1+, 2+, and 3+ is based on the intensity of DAB staining in each nucleus.
Microscopic view of original image
Microscopic view of analyzed image which shows excellent nuclei segmentation. Divisions into negative, 1+, 2+ and 3+ is configurable and can be changed to fit the research purpose.
Quantitative Output variables
The APP can output the following variables:
Step 1: Load an ER stained breast cancer image
Step 2: Outline tumor either manually or automatically using one of Visiopharm’s solutions
Step 3: Load and run the APP “10171 – ER, Breast Cancer, AI”
The APP uses a deep learning network, which has been trained using 13,434 annotated nuclei from IHC stained breast cancer tissue, to segment nuclei. The architectural structure of the network is a U-Net which is popular for medical image segmentation. The neural network uses a cascade of layers of nonlinear processing units for feature extraction and transformation, with each successive layer using the output from the previous layers as input. U-Net uses an encoder-decoder structure with a contracting path and an expansive path. For more information on the network architecture, see .
Estrogen Receptor, ER Status, Nuclei Segmentation, Immunohistochemistry, Breast Cancer, Artificial Intelligence, AI, Digital Pathology
1. Hammond, M. E., et al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer, Journal of Clinical Oncology, 2010, 28 (16), 2784-95, DOI
2. Kumar, N., et al. A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology, IEEE Transactions on Medical Imaging, 2017, 36 (7), 1550-1560, DOI
3. Ronneberger, O., et al. U-Net: Convolutional Networks for Biomedical Image Segmentation, . International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, 234-241, DOI