CAFR-CNN: coarse-to-fine adaptive faster R-CNN for cross-domain joint optic disc and cup segmentation

CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images

Accurate segmentation of the optic disk (OD) and optic cup (OC) regions of the optic nerve head is a critical step in glaucoma diagnosis. Existing architectures based on convolutional neural networks (CNNs) still suffer from insufficient global information and poor generalization ability to small sample datasets. Besides, advanced transformer-based models, although capable of capturing global image features, perform poorly in medical image segmentation due to numerous parameters and insufficient local spatial information. To address the above two problems, we propose an innovative W-shaped hybrid network framework, CC-TransXNet, which combines the advantages of CNN and transformer. Firstly, by employing TransXNet and improved ResNet as feature extraction modules, the network considers local and global features to enhance its generalization ability. Secondly, the convolutional block attention module (CBAM) is introduced in the residual structure to improve the ability to recognize the OD and OC by applying attention in both the channel and spatial dimensions. Thirdly, the Contextual Attention (CoT) self-attention mechanism is used in the skip connection to adaptively allocate attention to the contextual information, further enhancing the segmentation's accuracy. We conducted experiments on four publicly available datasets (REFUGE 2, RIM-ONE DL, GAMMA, and Drishti-GS). Compared with the traditional U-Net, CNN, and transformer-based networks, our proposed CC-TransXNet improves the segmentation accuracy and significantly enhances the generalization ability on small datasets. Moreover, CC-TransXNet effectively controls the number of parameters in the model through optimized design to avoid the risk of overfitting, proving its potential for efficient segmentation.

Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs

Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.