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Found 22 papers, 8 papers with code
Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data
Whole brain segmentation on a structural magnetic resonance imaging (MRI) is essential in non-invasive investigation for neuroanatomy.
3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles
To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location.
Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth
Herein, we proposed a novel end-to-end synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT image synthesis and CT splenomegaly segmentation simultaneously without using manual labels on CT.
SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth
SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality.
UNesT: Local Spatial Representation Learning with Hierarchical Transformer for Efficient Medical Segmentation
Transformer-based models, capable of learning better global dependencies, have recently demonstrated exceptional representation learning capabilities in computer vision and medical image analysis.
Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks
However, variations in both size and shape of the spleen on MRI images may result in large false positive and false negative labeling when deploying DCNN based methods.
Learning Site-specific Styles for Multi-institutional Unsupervised Cross-modality Domain Adaptation
In this paper, we present our solution to tackle the multi-institutional unsupervised domain adaptation for the crossMoDA 2023 challenge.
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
Cold-start AL is highly relevant in many practical scenarios but has been under-explored, especially for 3D medical segmentation tasks requiring substantial annotation effort.
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e. g., low contrast and large variations, but also the heterogeneity across tasks, i. e., one classification task for all views, and then one landmark detection task for each relevant view.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Skip connections in deep networks have improved both segmentation and classification performance by facilitating the training of deeper network architectures, and reducing the risks for vanishing gradients.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network.
Decompose to manipulate: Manipulable Object Synthesis in 3D Medical Images with Structured Image Decomposition
The performance of medical image analysis systems is constrained by the quantity of high-quality image annotations.
Class-Aware Adversarial Lung Nodule Synthesis in CT Images
Synthesizing the objects of interests, such as lung nodules, in medical images based on the distribution of annotated datasets can be helpful for improving the supervised learning tasks, especially when the datasets are limited by size and class balance.
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network.
Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT
Automated processing time to compute the severity scores was 10 seconds per case compared to 30 minutes required for manual annotations.
3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
Additionally, we leverage location priors derived from manually labeled COVID-19 chest CTs patients to generate appropriate abnormality distributions.
You May Need both Good-GAN and Bad-GAN for Anomaly Detection
A Bad-GAN generates pseudo anomalies at the low-density area of inlier distribution, and thus the inlier/outlier distinction can be approximated.
Characterizing Renal Structures with 3D Block Aggregate Transformers
Efficiently quantifying renal structures can provide distinct spatial context and facilitate biomarker discovery for kidney morphology.
A Comparative Study of Confidence Calibration in Deep Learning: From Computer Vision to Medical Imaging
We believe that this study has merits to guide readers to choose calibration models and understand gaps between general computer vision and medical imaging domains.
COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
We revisit the problem from a perspective of partial label supervision signals and identify two signals derived from ground truth and one from pseudo labels.
Flexible-Cm GAN: Towards Precise 3D Dose Prediction in Radiotherapy
Deep learning has been utilized in knowledge-based radiotherapy planning in which a system trained with a set of clinically approved plans is employed to infer a three-dimensional dose map for a given new patient.
