In this article, I will review the article ‘Tooth Detection and Segmentation with Mask R-CNN ’ published at the Second International Conference on Artificial Intelligence in Information and communication. This article describes the implementation of automatic tooth detection and segmentation on Mask RCNN’s dental images. The article, it is aimed to identify only females and divide them into segments.
It should be noted that Mask RCNN has a good segmentation effect even in complex and crowded dental structures ⚠️
If you are dealing in this area like me, the things we need to pay attention to first when reviewing an article will be keywords (keywords). The keywords in this article were selected as Mask R-CNN, Object Detection, Semantic Segmentation, and Tooth. We continue to do our research on these keywords.
A one-step network such as the Fully Convolutional Neural Network (FCN), You only Look Once (YOLO) and Single Shot multibox Detector (SSD) are 100-1000 times faster than the region-recommended algorithm , , .
❇️ Data Collection
Since there is no public data set, 100 images were collected from the hospital and the data set was trained. Of these images, 80 images are divided into educational data. The remaining 10 images are verification data, while the other 10 images are test data. Images of different distances and lighting and people of different sexes and ages were selected within the project. (Challenge for the network)
❇️ Tag Images Annotation
Labelme is an image tagging tool developed by MIT’s Computer Science and artificial intelligence laboratory (CSAIL) . Provides tools for tagging object edges. When annotating images, multiple polygons will form around the teeth. An example of this utility can be seen in Figure 1. Saves corner coordinates in a JSON file for an image. Since it is a manual operation, there will be a small error when annotating images. However, it does not affect the overall evaluation of the model. Since there is only one category, the tooth part is labeled as 1. The rest that is considered a background is labeled as 0.
❇️ Deep Network Architecture Details
You can see the Mask R-CNN architecture in the figure above. Mask R-CNN consists of several modules. Mask R-CNN, an extension of Faster-RCNN, includes a branch of convolution networks to perform the sample segmentation task. This branch is a standard convolutional neural network that serves as a feature extractor. In principle, this backbone network can be any network that extracts image features such as ResNet-50 or ResNet-101. In addition, to perform multi-scale detection, a feature pyramid network (FPN) is used in the backbone network. FPN improves the standard feature extraction pyramid by adding a second pyramid that takes the top-level features from the first pyramid and passes them to the lower layers. A deeper ResNet101 + FPN backbone was used in this project.
🔍 Details Of Architecture
A Roi align method for changing the ROI pool has been proposed. RoIAlign can maintain an approximate spatial position. RPN regression results are usually decimal and require integrals. The boxes obtained by RPN must be joined at the same maximum pooling size before entering the fully connected layer. During the project process, it was reported that the Integral was also needed, allowing RoIAlign to eliminate the integral process and protect the decimals. It is accurate for detection and segmentation. The classification combines the loss values of RoI regression and segmentation. Classification and ROI regression loss are no different from normal object detection networks. The mask loss branch is a convolutional neural network with ROI as the input and output is a small mask of size 28×28.
As the data will be trained at 50 epochs, 20 epochs of the data will be trained to start with, and 30 epochs will be trained to fine-tune all layers. The total loss value is 0.3093, consisting of bounding box loss, class loss, mask loss, and RPN loss. The total loss curve is shown in Figure 4. The final test result is also shown to be (a) the best result and (b) the worst.
The Pixel Accuracy (PA) method is the simplest and most effective method for evaluating results. The best result was 97.4% PA and the worst was 90.1%. Since there are a small number of prosthetic samples in the dental samples found in the project, the accuracy of prosthetic detection was low.
- Guohua Zhu, Zewen Piao, Suk Chan Kim, Department of Electronics Engineering, Pusan National University, Tooth Detection and Segmentation with Mask R-CNN, ICAIIC 2020.
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- B. Russell, A. Torralba, and W. T. Freeman, Labelme, The Open Annotation Tool MIT, Computer Science, and Artificial Intelligence Laboratory [Online]. Available: http://labelme.csail.mit.ed.
- Zhiming Cui, Changjian Li, Wenping Wang, The University of Hong Kong, ToothNet: Automatic Tooth Instance Segmentation and Identification from Cone Beam CT Images.