286 research outputs found
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Automatic detection of liver lesions in CT images poses a great challenge for
researchers. In this work we present a deep learning approach that models
explicitly the variability within the non-lesion class, based on prior
knowledge of the data, to support an automated lesion detection system. A
multi-class convolutional neural network (CNN) is proposed to categorize input
image patches into sub-categories of boundary and interior patches, the
decisions of which are fused to reach a binary lesion vs non-lesion decision.
For validation of our system, we use CT images of 132 livers and 498 lesions.
Our approach shows highly improved detection results that outperform the
state-of-the-art fully convolutional network. Automated computerized tools, as
shown in this work, have the potential in the future to support the
radiologists towards improved detection.Comment: To be presented at PatchMI: 3rd International Workshop on Patch-based
Techniques in Medical Imaging, MICCAI 201
Deep learning for image-based liver analysis â A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
Anatomical Data Augmentation For CNN based Pixel-wise Classification
In this work we propose a method for anatomical data augmentation that is
based on using slices of computed tomography (CT) examinations that are
adjacent to labeled slices as another resource of labeled data for training the
network. The extended labeled data is used to train a U-net network for a
pixel-wise classification into different hepatic lesions and normal liver
tissues. Our dataset contains CT examinations from 140 patients with 333 CT
images annotated by an expert radiologist. We tested our approach and compared
it to the conventional training process. Results indicate superiority of our
method. Using the anatomical data augmentation we achieved an improvement of 3%
in the success rate, 5% in the classification accuracy, and 4% in Dice.Comment: To be presented at IEEE ISBI 201
Medical Image Segmentation Review: The success of U-Net
Automatic medical image segmentation is a crucial topic in the medical domain
and successively a critical counterpart in the computer-aided diagnosis
paradigm. U-Net is the most widespread image segmentation architecture due to
its flexibility, optimized modular design, and success in all medical image
modalities. Over the years, the U-Net model achieved tremendous attention from
academic and industrial researchers. Several extensions of this network have
been proposed to address the scale and complexity created by medical tasks.
Addressing the deficiency of the naive U-Net model is the foremost step for
vendors to utilize the proper U-Net variant model for their business. Having a
compendium of different variants in one place makes it easier for builders to
identify the relevant research. Also, for ML researchers it will help them
understand the challenges of the biological tasks that challenge the model. To
address this, we discuss the practical aspects of the U-Net model and suggest a
taxonomy to categorize each network variant. Moreover, to measure the
performance of these strategies in a clinical application, we propose fair
evaluations of some unique and famous designs on well-known datasets. We
provide a comprehensive implementation library with trained models for future
research. In addition, for ease of future studies, we created an online list of
U-Net papers with their possible official implementation. All information is
gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine
Intelligence Journa
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection
An increasing number of public datasets have shown a marked impact on
automated organ segmentation and tumor detection. However, due to the small
size and partially labeled problem of each dataset, as well as a limited
investigation of diverse types of tumors, the resulting models are often
limited to segmenting specific organs/tumors and ignore the semantics of
anatomical structures, nor can they be extended to novel domains. To address
these issues, we propose the CLIP-Driven Universal Model, which incorporates
text embedding learned from Contrastive Language-Image Pre-training (CLIP) to
segmentation models. This CLIP-based label encoding captures anatomical
relationships, enabling the model to learn a structured feature embedding and
segment 25 organs and 6 types of tumors. The proposed model is developed from
an assembly of 14 datasets, using a total of 3,410 CT scans for training and
then evaluated on 6,162 external CT scans from 3 additional datasets. We rank
first on the Medical Segmentation Decathlon (MSD) public leaderboard and
achieve state-of-the-art results on Beyond The Cranial Vault (BTCV).
Additionally, the Universal Model is computationally more efficient (6x faster)
compared with dataset-specific models, generalized better to CT scans from
varying sites, and shows stronger transfer learning performance on novel tasks.Comment: Rank first in Medical Segmentation Decathlon (MSD) Competitio
Image Processing and Analysis for Preclinical and Clinical Applications
Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, âImage Processing and Analysis for Preclinical and Clinical Applicationsâ, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis
Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology
Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy.
Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patientsâ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on âcleanâ and âcontaminatedâ datasets.
The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction.
Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes.
Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liverâs Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well.
Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd
Generalizable deep learning based medical image segmentation
Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications.
To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques.
In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain.
For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios.
In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation.
In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method.
Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces
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