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Found 10 papers, 3 papers with code
Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision
We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks.
Improving Small Lesion Segmentation in CT Scans using Intensity Distribution Supervision: Application to Small Bowel Carcinoid Tumor
Our method can be one option for explicitly incorporating intensity distribution information of a target in network training.
Graph-Based Small Bowel Path Tracking with Cylindrical Constraints
To circumvent this, a series of cylinders that are fitted along the course of the small bowel are used to guide the tracking to more reliable directions.
Extraction of Coronary Vessels in Fluoroscopic X-Ray Sequences Using Vessel Correspondence Optimization
We present a method to extract coronary vessels from fluoroscopic x-ray sequences.
Deep Reinforcement Learning for Small Bowel Path Tracking using Different Types of Annotations
The proposed method holds a high degree of usability in this problem by being able to utilize the scans with weak annotations, and thus by possibly reducing the required annotation cost.
A Graph-theoretic Algorithm for Small Bowel Path Tracking in CT Scans
It is formulated as finding the minimum cost path between given start and end nodes on a graph that is constructed based on the bowel wall detection.
Unsupervised Domain Adaptation for Small Bowel Segmentation using Disentangled Representation
We present a novel unsupervised domain adaptation method for small bowel segmentation based on feature disentanglement.
Deep Small Bowel Segmentation with Cylindrical Topological Constraints
Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.
Deep Vessel Segmentation By Learning Graphical Connectivity
We propose a novel deep-learning-based system for vessel segmentation.
Ranked #1 on
Retinal Vessel Segmentation
on HRF
Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images
The results trained with only 10 strongly annotated images along with weakly annotated images were comparable to results trained from 800 strongly annotated images, with the 95% confidence interval of difference -3. 00%--5. 00%, in terms of the correct localization (CorLoc) measure, which is the ratio of images with intersection over union with ground truth higher than 0. 5.
