Abstract
Background The human epidermal growth factor receptor 2 (HER2) gene amplification status is crucial for developing a clinical strategy e.g. for evaluation of an anti-HER2-therapy in breast or stomach cancer. Therefore, the detection of HER2 gene amplification status is highly relevant in histopathological diagnostics. Recently, the application of convolutional neural networks (CNNs) has shown large progress in the automation of classification and object detection in medical image analysis.
Methods Here, we apply deep learning-based pipeline for the detection, localization and classification of interphase nuclei depending on their HER2 gene amplification state in Fluorescence in situ hybridization (FISH) images. Our pipeline combines two CNN architectures named RetinaNet which are trained on (1) the detection and classification of interphase nuclei into normal, low-grade and high-grade and on (2) the detection and classification of FISH signals into HER2 and into the centromere of chromosome 17 (CEN17). In the first step (RetinaNet-1) nuclei are localized image-wide and a first classification is applied.
The nuclei classification conducted via RetinaNet-1 is controlled and supplemented by HER2/CEN17 FISH signal ratios for the same nucleus by RetinaNet-2. Finally, an image-wide decision on the HER2 gene amplification stage is performed.
Results We demonstrate that the accuracy of this deep learning-based pipeline is on par with that of a pathologist. The pipeline accurately classifies FISH images as demonstrated on set of 57 validation images containing hundreds of nuclei. Consequently, high quality FISH images can now be analyzed at once regarding their image-wide HER2 gene amplification status in our lab.
Conclusions The automatic pipeline is a first step towards assisting the pathologist in evaluating the HER2 status of tumors using FISH images, for analyzing FISH images in retrospective and for optimizing the documentation of each tumor sample by automatically annotating and reporting of the HER2 gene amplification specificities.