A novel automation-assisted cervical cancer reading method based on convolutional neural network

Abstract

While automation-assisted reading system can improve efficiency, their performance often relies on the success of accurate cell segmentation and hand-craft feature extraction. This paper presents an efficient and totally segmentation-free method for automated cervical cell screening that utilizes modern object detector to directly detect cervical cells or clumps, without the design of specific hand-crafted feature. Specifically, we use the state-of-the-art CNN-based object detection methods, YOLOv3, as our baseline model. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade an additional task-specific classifier. We also investigate the presence of unreliable annotations and coped with them by smoothing the distribution of noisy labels. We comprehensively evaluate our methods on our test set which is consisted of 1014 annotated cervical cell images with size of 4000 × 3000 and complex cellular situation corresponding to 10 categories. Our model achieves 97.5% sensitivity (Sens) and 67.8% specificity (Spec) on cervical cell image-level screening. Moreover, we obtain a best mean average precision (mAP) of 63.4% on cervical cell-level diagnosis, and improve the average precision (AP) of hard examples which are the most valuable but most difficult to distinguish. Our automation-assisted cervical cell reading system not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. The results indicate feasible performance of our method, together with the efficiency and robustness, providing a new idea for future development of computer-assisted reading systems in clinical cervical screening.

Introduction

Cervical cancer is one of the most common causes of cancer death for women worldwide and is most frequently in developing countries [1]. Papanicolaou test (abbreviated as Pap test) or cervical cytology is now a mainstay cervical cancer screening method to detect potentially pre-cancerous and cancerous process in the cervix, which has demonstrated reduction in cervical cancer incidence and mortality in developed countries [2], [3]. Such method is performed by a visual examination of cytopathological analysis under the microscope of the glass slide and finally giving a diagnosis report according to the descriptive diagnosis method of the Bethesda system (TBS) [4]. However, manual analysis of microscope images is time-consuming, labor-intensive and error-prone as a handful of abnormal cells among millions of cells within a single slide has to be identified by a trained professional [5].

Therefore, automation-assisted screening based on cervical cytology has become a necessity. Since the first system was developed in 1950s, extensive research has attempted to exploit automation-assisted reading systems based on automatic image analysis techniques (e.g., [6], [7], [8]) which led to a couple of commercial systems emerged, such as the BD FocalPoint Slide Profiler [9] and ThinPrep [10] which received approval from the US Food and Drug Administration (FDA). While automation-assisted reading systems can increase productivity by reducing the time needed to read slides, their current performance and costs are not recommended for application in primary cervical screening [11], [12]. To this end, lots of automation-assisted methods based on cervical cell image analysis have been proposed [5], [13], [14], [15], [16]. Most of them follow the multi-stage pipeline, i.e., first identifying the candidate regions based on segmentation, then extracting hand-crafted features based on the characteristics of nuclei and cytoplasm for classification, as shown in Fig. 1(a).

While most of these studies have achieved available performance whether in cell segmentation or cell classification, there still have some challenges to use them in clinical automation-assisted reading. First, the current automation-assisted reading approaches has not been sufficiently cost-effective to promote to the cervical cell screening in clinical due to the tedious image patches preprocessing and screening process for cyto-technicians and doctors. The majority of existing research have done on the Herlve dataset [17] which only contains single-cell images with a size of 200 × 100 pixels approximately and was produced carefully by trained professionals. As shown in Fig. 2, images from Herlve dataset are all clear with no overlapping and impurity. In fact, the slide image with around 2160 million pixels obtained by the Whole Slide Imaging (WSI) technology [18] has complex cellular situation, such as cell overlapping, noise and impurity. Thus, one original cervical cell slide should be cropped into a huge number of single-cell image patches by using sliding windows or region proposal generation methods based on low-level image features, which lead to low efficiency. Second, it is difficult to make segmentation of the cytoplasm and nuclei absolutely due to the high degree of cell overlapping, the poor contrast of the cell cytoplasm and the presence of mucus, noise and impurity. Third, it is worth considering that whether the hand-crafted features can represent complex identification information or not since the richer semantic information sensitive to recognition may actually exist in hidden upper-level features of cervical cell images [19]. In addition, medical images are too complex and variable to get a perfect annotation as ground truth, which leads to noisy label unavoidably. However, the previous studies usually deal with cervical cell segmentation and classification without considering the existence of noisy labels.

To cope with these problems, we propose to utilize CNN-based object detection to automatically extract and learn task-specific features, and achieve the cervical cells recognition efficiently on multi-cell images with cell overlapping and clusters, as showing in Fig. 1(b). Our method directly operates on multi-cell image with size of 4000 × 3000 automatically extract more complex discriminative features and can obtain an image-level classification results. Moreover, it not only achieves cervical cell image-level classification but also provides more detailed location and category reference information of abnormal cells. In detail, we exploit YOLOv3 [20] as our cervical cell object detection baseline model due to the efficiency, accuracy and flexibility. In order to improve the classification performance of hard examples which are four highly similar categories, we cascade a further task-specifical classifier. Furthermore, we weaken the influence of noisy labels by smoothing their distribution.

Our contributions are summarized as follows: (1) Unlike the previous method, we treat the cervical cell recognition as object detection which automatically detect cervical cells directly on multi-cell images. It is more efficient as we can extract features automatically without manual intervention and careful design for all stages. Our method not only achieves cervical cell image-level classification but also provides more detailed location and category information of abnormal cells simultaneously. (2) We propose a simple and effective scheme, cascading a further task-specific classifier to improve the performance of hard example recognition. (3) We investigate the existence of noisy labels on cervical cell dataset and propose an approach to weaken the influence of them by smoothing their distribution.