ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet (2024)

Our research focuses on creating a framework for extracting 3D deformable objects from 2D scenes. We research the possibility of using multiple graph convolutional operators and depth estimators to extract the object, while also using predefined segmentation masks for the objects in the images. The experiments focus on a dataset from 2017, containing all…

Improving Unsupervised Graph-Based Skull Stripping: Enhancements and Comparative Analysis With State-Of-The-Art Methods (2024)

Brain disorders are increasingly prevalent today, making accurate brain segmentation essential for effective treatment andrecovery. This paperintroducesanenhancedunsupervisedgraph-basedbrainsegmentationmethod that employs an ellipsoid to select the nodes forming the graph. The method was rigorously evaluated on T1 and T2 modalities using four diverse datasets: the complete NFBS dataset, 48 MRIs from the IXI dataset, 16 images…

Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks’ ensemble (2024)

Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method: The first…

An Unsupervised Threshold-based GrowCut Algorithm for Mammography Lesion Detection (2022)

Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of GrowCut is applied to define the location…