Self-supervised pretraining has shown great performance in improving the accuracy of downstream tasks. Although pretraining on a large dataset improves performances, it becomes challenging to further optimize the model by solely enlarging the dataset. In contrast, additional adaptation of pretrained models to the target domain has shown promise in NLP. Inspired by the success of continual pretraining, we investigated the efficacy of adapting the target domain dataset to a pretrained model in medical imaging, particularly in the context of segmentation. We present a study based on a self-supervised pretraining framework using the SwinUNETR backbone. In this study, we improved the generalizability of the self-supervised pretraining by adapting a foundational model pretrained on 5k CT volumes to data of the downstream segmentation task. In detail, we employed 385 abdominal CT volumes for the continual task-adaptive pretraining and 24 abdominal CT volumes for the downstream segmentation task, all sourced from the same dataset. Additionally, we conducted comparative experiments to demonstrate the benefits of this task-adapting pretraining approach. Our method has shown that continual pretraining helps to improve the performances, achieving an average Dice score for 10-class organ segmentation of 87.8%.
The purpose of this paper is to introduce a practical framework of using proxy data in automatic hyperparameter
optimization for 3D multi-organ segmentation. The automated segmentation of abdominal organs from CT
volumes is a main task in the medical image analysis field. Much research has been investigated to handle this task
based on the immense experience of machine learning. Deep learning approaches require enormous experiments
to design the optimal configurations for the best performance. Automatic machine learning (AutoML) using
hyperparameter optimization to search the optimal training strategy makes it possible to find the appropriate
settings without much deep experience. However, biases of training data can be highly related to the AutoML
performance and efficiency. In this paper, we propose an AutoML framework that uses pre-selected proxy data
to represent the entire dataset which has the potential to reduce the computation time needed for efficient
hyperparameter optimization in searching learning. Both quantitative and qualitative results showed that our
framework can effectively build more powerful segmentation models than manually designed deep-learning-based
methods and AutoML, which use carefully tuned hyperparameters and randomly selected training subsets,
respectively. The average Dice score for 10-class abdominal organ segmentation was 85.9%.
This paper proposes an unpaired medical image translation framework between portal-venous phase and non-contrast CT volumes. Image-to-image translation has immense potential application values in medical image analysis fields, such as segmentation. Currently, many deep learning-based segmentation methods have been proposed on contrast-enhanced CT volumes. However, for the patients who have contrast medium allergy, only non-contrast CT is available. Thus, segmentation using non-contrast CT volumes is also an important task. Image translation from non-contrast CT to contrast-enhanced CT is an alternative to solve this problem. In this work, we employed the cycle-consistent adversarial network (CycleGAN) and unpaired image-to-image network (UNIT) for image translation. To evaluate the translation performance for multi-organ segmentation, we trained a segmentation model using contrast-enhanced CT images with U-Net. Our experimental results show that image translation has a positive influence on multi-organ segmentation. The segmentation actuaries greatly improved by applying the image translation.
This paper presents a method for extracting the lung and lesion regions from COVID-19 CT volumes using 3D fully convolutional networks. Due to the pandemic of coronavirus disease 2019 (COVID-19), computer aided diagnosis (CAD) system for COVID-19 using CT volume is required. In the development of CAD system, it is important to extract patient anatomical structures in CT volume. Therefore, we develop a method for extracting the lung and lesion regions from COVID-19 CT volumes for the CAD system of COVID-19. We use 3D U-Net type fully convolutional network (FCN) for extraction of the lung and lesion regions. We also use transfer learning to train the 3D U-Net type FCN using the limited data of COVID-19 CT volume. As pre-training, the proposed method trains the 3D U-Net model using abdominal multi-organ regions segmentation dataset which contains a large number of annotated CT volumes. After pre-training, we train the 3D U-Net model from the pre-trained model using a small number of annotated COVID-19 CT volumes. The experimental results showed that the proposed method could extract the lung and lesion regions from COVID-19 CT volumes.
This paper presents segmentation of multiple organ regions from non-contrast CT volume based on deep learning. Also, we report usefulness of fine-tuning using a small number of training data for multi-organ regions segmentation. In medical image analysis system, it is vital to recognize patient specific anatomical structures in medical images such as CT volumes. We have studied on a multi-organ regions segmentation method from contrast-enhanced abdominal CT volume using 3D U-Net. Since non-contrast CT volumes are also usually used in the medical field, segmentation of multi-organ regions from non-contrast CT volume is also important for the medical image analysis system. In this study, we extract multi-organ regions from non-contrast CT volume using 3D U-Net and a small number of training data. We perform fine-tuning from a pre-trained model obtained from the previous studies. The pre-trained 3D U-Net model is trained by a large number of contrast enhanced CT volumes. Then, fine-tuning is performed using a small number of non-contrast CT volumes. The experimental results showed that the fine-tuned 3D U-Net model could extract multi-organ regions from non-contrast CT volume. The proposed training scheme using fine-tuning is useful for segmenting multi-organ regions using a small number of training data.
The purpose of this paper is to present multi-organ segmentation method using spatial information-embedded fully convolutional networks (FCNs). Semantic segmentation of major anatomical structure from CT volumes is promising to apply in clinical work ows. A multitude of deep-learning-based approaches have been proposed for 3D image processing. With the rapid development of FCNs, the encoder-decoder network architecture is proved to achieved acceptable performance on segmentation tasks. However, it is hard to obtain the spatial information from sub-volumes during training. In this paper, we extend the spatial position information-embeded FCNs which designed for binary segmentation tor multi-class organ segmentation. We introduced gamma correction in data augmentation to improve the FCNs robustness. We compared the FCNs performance with different normalization methods, including batch normalization and instance normalization. Experiment results showed that our modifications positively influence the segmentation performance on abdominal CT dataset. Our highest average dice score achieves 87.2%, while the previous method achieved 86.2%.
KEYWORDS: Image segmentation, 3D image processing, Data modeling, Medical imaging, Computed tomography, Image processing, Arteries, Veins, 3D modeling, Network architectures
Segmentation is one of the most important tasks in medical image analysis. With the development of deep leaning, fully convolutional networks (FCNs) have become the dominant approach for this task and their extension to 3D achieved considerable improvements for automated organ segmentation in volumetric imaging data, such as computed tomography (CT). One popular FCN network architecture for 3D volumes is V-Net, originally proposed for single region segmentation. This network effectively solved the imbalance problem between foreground and background voxels by proposing a loss function based on the Dice similarity metric. In this work, we extend the depth of the original V-Net to obtain better features to model the increased complexity of multi-class segmentation tasks at higher input/output resolutions using modern large-memory GPUs. Furthermore, we markedly improved the training behaviour of V-Net by employing batch normalization layers throughout the network. In this way, we can efficiently improve the stability of the training optimization, achieving faster and more stable convergence. We show that our architectural changes and refinements dramatically improve the segmentation performance on a large abdominal CT dataset and obtain close to 90% average Dice score.
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