Integrating Edge Information into Ground Truth for the Segmentation of the Optic Disc and Cup from Fundus Images
Optic disc and cup segmentation helps in the diagnosis of glaucoma, myocardial infarction, and diabetic retinopathy. Most deep learning methods developed to perform segmentation tasks are built on top of a U-Net-based model architecture. Nevertheless, U-Net and its variants have a tendency to over-segment/ under-segment the required regions of interest. Since the most important outcome is the value of cup-to-disc ratio and not the segmented regions themselves, we are more concerned about the boundaries rather than the regions under the boundaries. This makes learning edges important as compared to learning the regions. In the proposed work, the authors aim to extract both edges of the optic disc and cup from the ground truth using a Laplacian filter. Next, edges are reconstructed to obtain an edge ground truth in addition to the optic disc-cup ground truth. Utilizing both ground truths, the authors study several U-Net and its variant architectures with and without optic disc and cup edges as target, along with the optic disc-cup ground truth for segmentation. The authors have used the REFUGE benchmark dataset and the Drishti-GS dataset to perform the study, and the results are tabulated for the dice and the Hausdorff distance metrics. In the case of the REFUGE dataset, the optic disc mean dice score has improved from 0.7425 to 0.8859 while the mean Hausdorff distance has reduced from 6.5810 to 3.0540 for the baseline U-Net model. Similarly, the optic cup mean dice score has improved from 0.6970 to 0.8639 while the mean Hausdorff distance has reduced from 5.2340 to 2.6323 for the same model. Similar improvement has been observed for the Drishti-GS dataset as well. Compared to the baseline U-Net and its variants (i.e) the Attention U-Net and the U-Net++, the models that learn integrated edges along with the optic disc and cup regions performed well in both validation and testing datasets.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Integrating Edges into U-Net Models with Explainable Activation Maps for Brain Tumor Segmentation using MR Images
Manual delineation of tumor regions from magnetic resonance (MR) images is time-consuming, requires an expert, and is prone to human error. In recent years, deep learning models have been the go-to approach for the segme…
Brain Tumor SegmentationSemantic SegmentationTumor SegmentationLayoutMP3D: Layout Annotation of Matterport3D
Inferring the information of 3D layout from a single equirectangular panorama is crucial for numerous applications of virtual reality or robotics (e.g., scene understanding and navigation). To achieve this, several datas…
Scene UnderstandingDecoupled Seg Tokens Make Stronger Reasoning Video Segmenter and Grounder
Existing video segmenter and grounder approaches, exemplified by Sa2VA, directly fuse features within segmentation models. This often results in an undesirable entanglement of dynamic visual information and static semant…
Image SegmentationLarge Language ModelQuestion AnsweringSegmentation+5Evaluation of Pipelines for Data Integration into Knowledge Graphs
Integrating new data into knowledge graphs (KG) typically involves different tasks that are executed within workflows or pipelines There are many possible pipelines for a specific integration problem but there is not yet…
Knowledge GraphsSelf-consistent Deep Geometric Learning for Heterogeneous Multi-source Spatial Point Data Prediction
Multi-source spatial point data prediction is crucial in fields like environmental monitoring and natural resource management, where integrating data from various sensors is the key to achieving a holistic environmental …
Graph Neural Network