Image quality assessment of CT scans is of utmost importance in balancing radiation dose and image quality. Nonetheless, estimating the image quality of CT scans is a highly subjective task that cannot be adequately captured by a single quantitative metric. In this work, we present a novel vision Transformer network for no-reference CT image quality assessment. Our network combines convolutional operations and multi-head self-attention mechanisms by adding a powerful convolutional stem in the beginning of the traditional ViT network. To enhance the performance and efficiency of the network, we introduce a distillation methodology, comprised of two sequential steps. In Step I, we construct a “teacher ensemble network” by training five Vision Transformer networks using a five-fold division schema. In Step II, we train a single vision Transformer, referred to as the “student network”, by using the teacher’s predictions as new labels. The student network is also optimized using the original labeled dataset. The effectiveness of the proposed model is evaluated on the task of predicting image quality scores from low-dose abdominal CT images from the LDCTIQAC2023 Grand Challenge. Our model demonstrates remarkable performance, ranking 6th during the testing phase of the challenge. Additionally, our experiments highlight the effectiveness of incorporating a convolutional stem in the ViT architecture and the distillation methodology.
KEYWORDS: Image segmentation, Cochlea, Data modeling, Gallium nitride, Magnetic resonance imaging, Brain, Performance modeling, Medical imaging, Anatomy, Digital image processing, Adversarial training
Convolutional neural networks (CNNs) have achieved great success in automating the segmentation of medical images. Nevertheless, when a trained CNN is tested on a new domain there is a performance degradation due to the distribution shift. In this work, we present an unsupervised Extensive Pixel-level Augmentation framework (EPA) for cross-modality domain adaptation. EPA implements a two-phase image- and feature-level adaptation method. In the first phase, the source domain images are mapped to target domain in pixel space using the CycleGAN, StAC-DA, and CUT translation models. This creates an augmented translated dataset 3 times bigger than the original. In phase 2, a deeply supervised U-Net network is trained to segment the target images using a semi-supervised adversarial learning approach. In particular, a set of discriminator networks are trained to distinguish between the target and source domain segmentations, while the U-Net aims to fool them. EPA is tested on the task of brain structure segmentation from the Crossmoda 2022 Grand Challenge, being ranked within the top 12 submissions of the testing phase. Moreover, we demonstrate that augmenting the size of the mapped dataset through distinct translation methods is crucial for increasing the segmentation accuracy of the model.
KEYWORDS: Image segmentation, Medical imaging, Magnetic resonance imaging, Machine learning, Brain, Performance modeling, Data modeling, Target acquisition, Systems modeling
Deep learning models have obtained state-of-the-art results for medical image analysis. However, CNNs require a massive amount of labelled data to achieve a high performance. Moreover, many supervised learning approaches assume that the training/source dataset and test/target dataset follow the same probability distribution. Nevertheless, this assumption is hardly satisfied in real-world data and when the models are tested on an unseen domain there is a significant performance degradation. In this work, we present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image segmentation. C-MADA implements an image-level and feature-level adaptation method in a two-step sequential manner. First, images from the source domain are translated to the target domain through an unpaired image-to-image adversarial translation with cycle-consistency loss. Then, a U-Net network is trained with the mapped source domain images and target domain images in an adversarial manner to learn domain-invariant feature representations and produce segmentations for the target domain. Furthermore, to improve the network’s segmentation performance, information about the shape, texture, and contour of the predicted segmentation is included during the adversarial training. C-MADA is tested on the task of brain MRI segmentation from the crossMoDa Grand Challenge and is ranked within the top 15 submissions of the challenge.
Deep learning plays a critical role in medical image segmentation. Nevertheless, manually designing a neural network for a specific segmentation problem is a very difficult and time-consuming task due to the massive hyperparameter search space, long training time and large volumetric data. Therefore, most designed networks are highly complex, task specific and over-parametrized. Recently, multiobjective neural architecture search (NAS) methods have been proposed to automate the design of accurate and efficient segmentation architectures. However, they only search for either the macro- or micro-structure of the architecture, do not use the information produced during the optimization process to increase the efficiency of the search, and do not consider the volumetric nature of medical images. In this work, we propose EMONAS, an Efficient MultiObjective Neural Architecture Search framework for 3D medical image segmentation. EMONAS is composed of a search space that considers both the macro- and micro-structure of the architecture, and a surrogate-assisted multiobjective evolutionary based algorithm that efficiently searches for the best hyperparameters using a Random Forest surrogate and guiding selection probabilities. EMONAS is evaluated on the task of cardiac segmentation from the ACDC MICCAI challenge. The architecture found is ranked within the top 10 submissions in all evaluation metrics, performing better or comparable to other approaches while reducing the search time by more than 50% and having considerably fewer number of parameters.
Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models achieving state-of-the art results in various medical image datasets. Network architectures are usually designed manually for a specific segmentation task so applying them to other medical datasets requires extensive expertise and time. Moreover, the segmentation requires handling large volumetric data that results in big and complex architectures. Recently, methods that automatically design neural networks for medical image segmentation have been presented; however, most approaches either do not fully consider volumetric information or do not optimize the size of the network. In this paper, we propose a novel self-adaptive 2D-3D ensemble of FCNs for 3D medical image segmentation that incorporates volumetric information and optimizes both the models performance and size. The model is composed of an ensemble of a 2D FCN that extracts intra-slice and long-range information, and a 3D FCN that exploits inter-slice information. The architectures of the 2D and 3D FCNs are automatically adapted to a medical image dataset using a multiobjective evolutionary based algorithm that minimizes both the expected segmentation error and number of parameters in the network. The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentation on the image dataset from the PROMISE12 Grand Challenge. The resulting network is ranked in the top 10 submissions, surpassing the performance of other automatically-designed architectures while having 13.3x fewer parameters.
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