266 research outputs found
Special issue on MICCAI 2018
status: publishe
Guest Editorial: IJCARS-MICCAI 2018 special issue.
status: publishe
Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Organ at Risk (OAR) segmentation from CT scans is a key component of the
radiotherapy treatment workflow. In recent years, deep learning techniques have
shown remarkable potential in automating this process. In this paper, we
investigate the performance of Generative Adversarial Networks (GANs) compared
to supervised learning approaches for segmenting OARs from CT images. We
propose three GAN-based models with identical generator architectures but
different discriminator networks. These models are compared with
well-established CNN models, such as SE-ResUnet and DeepLabV3, using the
StructSeg dataset, which consists of 50 annotated CT scans containing contours
of six OARs. Our work aims to provide insight into the advantages and
disadvantages of adversarial training in the context of OAR segmentation. The
results are very promising and show that the proposed GAN-based approaches are
similar or superior to their CNN-based counterparts, particularly when
segmenting more challenging target organs
Probabilistic 3D surface reconstruction from sparse MRI information
Surface reconstruction from magnetic resonance (MR) imaging data is
indispensable in medical image analysis and clinical research. A reliable and
effective reconstruction tool should: be fast in prediction of accurate well
localised and high resolution models, evaluate prediction uncertainty, work
with as little input data as possible. Current deep learning state of the art
(SOTA) 3D reconstruction methods, however, often only produce shapes of limited
variability positioned in a canonical position or lack uncertainty evaluation.
In this paper, we present a novel probabilistic deep learning approach for
concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric
uncertainty prediction. Our method is capable of reconstructing large surface
meshes from three quasi-orthogonal MR imaging slices from limited training sets
whilst modelling the location of each mesh vertex through a Gaussian
distribution. Prior shape information is encoded using a built-in linear
principal component analysis (PCA) model. Extensive experiments on cardiac MR
data show that our probabilistic approach successfully assesses prediction
uncertainty while at the same time qualitatively and quantitatively outperforms
SOTA methods in shape prediction. Compared to SOTA, we are capable of properly
localising and orientating the prediction via the use of a spatially aware
neural network.Comment: MICCAI 202
Domain Adaptive Synapse Detection with Weak Point Annotations
The development of learning-based methods has greatly improved the detection
of synapses from electron microscopy (EM) images. However, training a model for
each dataset is time-consuming and requires extensive annotations.
Additionally, it is difficult to apply a learned model to data from different
brain regions due to variations in data distributions. In this paper, we
present AdaSyn, a two-stage segmentation-based framework for domain adaptive
synapse detection with weak point annotations. In the first stage, we address
the detection problem by utilizing a segmentation-based pipeline to obtain
synaptic instance masks. In the second stage, we improve model generalizability
on target data by regenerating square masks to get high-quality pseudo labels.
Benefiting from our high-accuracy detection results, we introduce the distance
nearest principle to match paired pre-synapses and post-synapses. In the
WASPSYN challenge at ISBI 2023, our method ranks the 1st place
SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation
Microscopic image segmentation is a challenging task, wherein the objective
is to assign semantic labels to each pixel in a given microscopic image. While
convolutional neural networks (CNNs) form the foundation of many existing
frameworks, they often struggle to explicitly capture long-range dependencies.
Although transformers were initially devised to address this issue using
self-attention, it has been proven that both local and global features are
crucial for addressing diverse challenges in microscopic images, including
variations in shape, size, appearance, and target region density. In this
paper, we introduce SA2-Net, an attention-guided method that leverages
multi-scale feature learning to effectively handle diverse structures within
microscopic images. Specifically, we propose scale-aware attention (SA2) module
designed to capture inherent variations in scales and shapes of microscopic
regions, such as cells, for accurate segmentation. This module incorporates
local attention at each level of multi-stage features, as well as global
attention across multiple resolutions. Furthermore, we address the issue of
blurred region boundaries (e.g., cell boundaries) by introducing a novel
upsampling strategy called the Adaptive Up-Attention (AuA) module. This module
enhances the discriminative ability for improved localization of microscopic
regions using an explicit attention mechanism. Extensive experiments on five
challenging datasets demonstrate the benefits of our SA2-Net model. Our source
code is publicly available at \url{https://github.com/mustansarfiaz/SA2-Net}.Comment: BMVC 2023 accepted as ora
Improving the domain generalization and robustness of neural networks for medical imaging
Deep neural networks are powerful tools to process medical images, with great potential to accelerate clinical workflows and facilitate large-scale studies. However, in order to achieve satisfactory performance at deployment, these networks generally require massive labeled data collected from various domains (e.g., hospitals, scanners), which is rarely available in practice. The main goal of this work is to improve the domain generalization and robustness of neural networks for medical imaging when labeled data is limited.
First, we develop multi-task learning methods to exploit auxiliary data to enhance networks. We first present a multi-task U-net that performs image classification and MR atrial segmentation simultaneously. We then present a shape-aware multi-view autoencoder together with a multi-view U-net, which enables extracting useful shape priors from complementary long-axis views and short-axis views in order to assist the left ventricular myocardium segmentation task on the short-axis MR images. Experimental results show that the proposed networks successfully leverage complementary information from auxiliary tasks to improve model generalization on the main segmentation task.
Second, we consider utilizing unlabeled data. We first present an adversarial data augmentation method with bias fields to improve semi-supervised learning for general medical image segmentation tasks. We further explore a more challenging setting where the source and the target images are from different data distributions. We demonstrate that an unsupervised image style transfer method can bridge the domain gap, successfully transferring the knowledge learned from labeled balanced Steady-State Free Precession (bSSFP) images to unlabeled Late Gadolinium Enhancement (LGE) images, achieving state-of-the-art performance on a public multi-sequence cardiac MR segmentation challenge.
For scenarios with limited training data from a single domain, we first propose a general training and testing pipeline to improve cardiac image segmentation across various unseen domains. We then present a latent space data augmentation method with a cooperative training framework to further enhance model robustness against unseen domains and imaging artifacts.Open Acces
Data efficient deep learning for medical image analysis: A survey
The rapid evolution of deep learning has significantly advanced the field of
medical image analysis. However, despite these achievements, the further
enhancement of deep learning models for medical image analysis faces a
significant challenge due to the scarcity of large, well-annotated datasets. To
address this issue, recent years have witnessed a growing emphasis on the
development of data-efficient deep learning methods. This paper conducts a
thorough review of data-efficient deep learning methods for medical image
analysis. To this end, we categorize these methods based on the level of
supervision they rely on, encompassing categories such as no supervision,
inexact supervision, incomplete supervision, inaccurate supervision, and only
limited supervision. We further divide these categories into finer
subcategories. For example, we categorize inexact supervision into multiple
instance learning and learning with weak annotations. Similarly, we categorize
incomplete supervision into semi-supervised learning, active learning, and
domain-adaptive learning and so on. Furthermore, we systematically summarize
commonly used datasets for data efficient deep learning in medical image
analysis and investigate future research directions to conclude this survey.Comment: Under Revie
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