A real-time computer-aided diagnosis method for hydatidiform mole recognition using deep neural network

Abstract

Background and objective: Hydatidiform mole (HM) is one of the most common gestational trophoblastic diseases with malignant potential. Histopathological examination is the primary method for diagnosing HM. However, due to the obscure and confusing pathology features of HM, significant observer variability exists among pathologists, leading to over- and misdiagnosis in clinical practice. Efficient feature extraction can significantly improve the accuracy and speed of the diagnostic process. Deep neural network (DNN) has been proven to have excellent feature extraction and segmentation capabilities, which is widely used in clinical practice for many other diseases. We constructed a deep learning-based CAD method to recognize HM hydrops lesions under the microscopic view in real-time.

Methods: To solve the challenge of lesion segmentation due to difficulties in extracting effective features from HM slide images, we proposed a hydrops lesion recognition module that employs DeepLabv3+ with our novel compound loss function and a stepwise training strategy to achieve great performance in recognizing hydrops lesions at both pixel and lesion level. Meanwhile, a Fourier transform-based image mosaic module and an edge extension module for image sequences were developed to make the recognition model more applicable to the case of moving slides in clinical practice. Such an approach also addresses the situation where the model has poor results for image edge recognition.

Results: We evaluated our method using widely adopted DNNs on an HM dataset and chose DeepLabv3+ with our compound loss function as the segmentation model. The comparison experiments show that the edge extension module is able to improve the model performance by at most 3.4% regarding pixel-level IoU and 9.0% regarding lesion-level IoU. As for the final result, our method is able to achieve a pixel-level IoU of 77.0%, a precision of 86.0%, and a lesion-level recall of 86.2% while having a response time of 82 ms per frame. Experiments show that our method is able to display the full microscopic view with accurately labeled HM hydrops lesions following the movement of slides in real-time.

Conclusions: To the best of our knowledge, this is the first method to utilize deep neural networks in HM lesion recognition. This method provides a robust and accurate solution with powerful feature extraction and segmentation capabilities for auxiliary diagnosis of HM.

Keywords: Computer-aided diagnosis; Deep learning; Hydatidiform mole; Image segmentation; Pathology.