Search Results for author:
Found 37 papers, 8 papers with code
The Minimum Cost Connected Subgraph Problem in Medical Image Analysis
Several important tasks in medical image analysis can be stated in the form of an optimization problem whose feasible solutions are connected subgraphs.
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
Efficient Algorithms for Moral Lineage Tracing
Lineage tracing, the joint segmentation and tracking of living cells as they move and divide in a sequence of light microscopy images, is a challenging task.
A Localisation-Segmentation Approach for Multi-label Annotation of Lumbar Vertebrae using Deep Nets
The first stage employs a multi-layered perceptron performing non-linear regression for locating the lumbar region using the global context.
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
The segmentation of liver lesions is crucial for detection, diagnosis and monitoring progression of liver cancer.
Deep-FExt: Deep Feature Extraction for Vessel Segmentation and Centerline Prediction
Feature extraction is a very crucial task in image and pixel (voxel) classification and regression in biomedical image modeling.
DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes
Our experiments show that, by replacing 3-D filters with cross-hair filters in our network, we achieve over 23% improvement in speed, lower memory footprint, lower network complexity which prevents overfitting and comparable accuracy (with a Cox-Wilcoxon paired sample significance test p-value of 0. 07 when compared to full 3-D filters).
Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior
Robust localisation and identification of vertebrae is essential for automated spine analysis.
Multi-level Activation for Segmentation of Hierarchically-nested Classes
For many biological image segmentation tasks, including topological knowledge, such as the nesting of classes, can greatly improve results.
DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual Learning
Arterial spin labeling (ASL) allows to quantify the cerebral blood flow (CBF) by magnetic labeling of the arterial blood water.
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss
Dynamic contrast-enhanced (DCE) MRI is an evolving imaging technique that provides a quantitative measure of pharmacokinetic (PK) parameters in body tissues, in which series of T1-weighted images are collected following the administration of a paramagnetic contrast agent.
Qunatification of Metabolites in MR Spectroscopic Imaging using Machine Learning
The goal of our proposed framework is to learn the spectral features from a training set comprising of different variations of both simulated and in-vivo brain spectra and then use this learning for the subsequent metabolite quantification.
Why rankings of biomedical image analysis competitions should be interpreted with care
International challenges have become the standard for validation of biomedical image analysis methods.
Hierarchical multi-class segmentation of glioma images using networks with multi-level activation function
Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.
Group-Attention Single-Shot Detector (GA-SSD): Finding Pulmonary Nodules in Large-Scale CT Images
In this paper, we propose a novel and effective abnormality detector implementing the attention mechanism and group convolution on 3D single-shot detector (SSD) called group-attention SSD (GA-SSD).
Computed Tomography (CT)
Finding Pulmonary Nodules In Large-Scale Ct Images
Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy
Furthermore, we explored two variants of adversarial training schemes that incorporated the anatomical a priori knowledge into the Btrfly Net.
Cross-view Relation Networks for Mammogram Mass Detection
Mammogram is the most effective imaging modality for the mass lesion detection of breast cancer at the early stage.
Probabilistic Point Cloud Reconstructions for Vertebral Shape Analysis
We propose an auto-encoding network architecture for point clouds (PC) capable of extracting shape signatures without supervision.
Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting
Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues.
clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Deep Reinforcement Learning for Organ Localization in CT
Robust localization of organs in computed tomography scans is a constant pre-processing requirement for organ-specific image retrieval, radiotherapy planning, and interventional image analysis.
Inferring the 3D Standing Spine Posture from 2D Radiographs
The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D.
Grading Loss: A Fracture Grade-based Metric Loss for Vertebral Fracture Detection
Building on state-of-art metric losses, we present a novel Grading Loss for learning representations that respect Genant's fracture grading scheme.
Cranial Implant Prediction using Low-Resolution 3D Shape Completion and High-Resolution 2D Refinement
The first subnetwork is designed to complete the shape of the downsampled defective skull.
Robustification of Segmentation Models Against Adversarial Perturbations In Medical Imaging
This paper presents a novel yet efficient defense framework for segmentation models against adversarial attacks in medical imaging.
METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy
Novel multimodal imaging methods are capable of generating extensive, super high resolution datasets for preclinical research.
Evaluating the Robustness of Self-Supervised Learning in Medical Imaging
Self-supervision has demonstrated to be an effective learning strategy when training target tasks on small annotated data-sets.
clDice - A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
Semi-Implicit Neural Solver for Time-dependent Partial Differential Equations
In this paper, we propose a neural solver to learn an optimal iterative scheme in a data-driven fashion for any class of PDEs.
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images
First, it is time-consuming - the clinician needs to scan through several slices of images to ascertain the region of interest before deciding on what severity grade to assign to a patient.
A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning.
Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs
Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.
Primitive Simultaneous Optimization of Similarity Metrics for Image Registration
Even though simultaneous optimization of similarity metrics is a standard procedure in the field of semantic segmentation, surprisingly, this is much less established for image registration.
Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations
To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data.
Framing image registration as a landmark detection problem for better representation of clinical relevance
Nowadays, registration methods are typically evaluated based on sub-resolution tracking error differences.
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images, e. g., for the evaluation of volumetric changes.
