Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

We aim at segmenting small organs (e.g., the pancreas) from abdominal CT scans. As the target often occupies a relatively small region in the input image, deep neural networks can be easily confused by the complex and variable background. To alleviate this, researchers proposed a coarse-to-fine approach, which used prediction from the first (coarse) stage to indicate a smaller input region for the second (fine) stage. Despite its effectiveness, this algorithm dealt with two stages individually, which lacked optimizing a global energy function, and limited its ability to incorporate multi-stage visual cues. Missing contextual information led to unsatisfying convergence in iterations, and that the fine stage sometimes produced even lower segmentation accuracy than the coarse stage. This paper presents a Recurrent Saliency Transformation Network. The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration. This brings us two-fold benefits. In training, it allows joint optimization over the deep networks dealing with different input scales. In testing, it propagates multi-stage visual information throughout iterations to improve segmentation accuracy. Experiments in the NIH pancreas segmentation dataset demonstrate the state-of-the-art accuracy, which outperforms the previous best by an average of over 2%. Much higher accuracies are also reported on several small organs in a larger dataset collected by ourselves. In addition, our approach enjoys better convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures

A Fully Automated Approach to Segmentation of Irregularly Shaped Cellular Structures in EM Images

While there has been substantial progress in segmenting natural im-ages, state-of-the-art methods that perform well in such tasks unfortunately tend to underperform when confronted with the different challenges posed by electron microscope (EM) data. For example, in EM imagery of neural tissue, numerous cells and subcellular structures appear within a single image, they exhibit irregular shapes that cannot be easily modeled by standard techniques, and confusing textures clutter the background. We propose a fully automated approach that handles these challenges by using sophisticated cues that capture global shape and texture information, and by learning the specific appearance of object boundaries. We demonstrate that our approach significantly outperforms state-of-the-art techniques and closely matches the performance of human annotators

4D Image Analysis and Diagnosis of Kidney Disease Using DCE-MRI Images

Abstract Because of noninvasive nature, medical imaging is easy to perform though it is extravagant. For furnishing superior anatomy and decisiveness, different characteristics have been extrapolated from intake image. Earlier the processing steps like registration, segmentation are separately applied for extraction of sequential proprieties of DCE-MRI images of kidney. For simultaneous registration and segmentation of the kidney, a 4D model is described. In the conscript of kidney abnormal functioning and disease detection, the glomerular filtration rate (GFR) is a significant factor. Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) is the imaging proficiency, used for calibrating different parameters homologous to suffuse, capillary leakage, and convey rate in tissues of various organs and diseases detection. The described technique's approach permits us to automatically accomplishing a statistical analysis of various parameters from alive cells. Conclusion of findings is accomplished by average gray level intensity inside the kidney region

Fast and robust hybrid framework for infant brain classification from structural MRI : a case study for early diagnosis of autism.

The ultimate goal of this work is to develop a computer-aided diagnosis (CAD) system for early autism diagnosis from infant structural magnetic resonance imaging (MRI). The vital step to achieve this goal is to get accurate segmentation of the different brain structures: whitematter, graymatter, and cerebrospinal fluid, which will be the main focus of this thesis. The proposed brain classification approach consists of two major steps. First, the brain is extracted based on the integration of a stochastic model that serves to learn the visual appearance of the brain texture, and a geometric model that preserves the brain geometry during the extraction process. Secondly, the brain tissues are segmented based on shape priors, built using a subset of co-aligned training images, that is adapted during the segmentation process using first- and second-order visual appearance features of infant MRIs. The accuracy of the presented segmentation approach has been tested on 300 infant subjects and evaluated blindly on 15 adult subjects. The experimental results have been evaluated by the MICCAI MR Brain Image Segmentation (MRBrainS13) challenge organizers using three metrics: Dice coefficient, 95-percentile Hausdorff distance, and absolute volume difference. The proposed method has been ranked the first in terms of performance and speed

Sparse feature learning for image analysis in segmentation, classification, and disease diagnosis.

The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep models, and Alzheimer\u27s disease classification. Nonnegative Matrix Factorization, Autoencoder and 3D Convolutional Autoencoder are used as architectures or models for unsupervised feature learning. They are investigated along with nonnegativity, sparsity and part-based representation constraints for generalized and transferable feature extraction

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) imaging of neural tissue. EM microscopy is one of the key tools used to analyze neural tissue and understand the brain, but the huge amounts of data it produces make automated analysis necessary. In addition to the challenges specific to EM data, the common problems encountered in image segmentation must also be addressed. These problems include extracting discriminative features from the data and constructing a statistical model using ground-truth data. Although complex models appear to be more attractive because they allow for more expressiveness, they also lead to a higher computational complexity. On the other hand, simple models come with a lower complexity but less faithfully express the real world. Therefore, one of the most challenging tasks in image segmentation is in constructing models that are expressive enough while remaining tractable. In this work, we propose several automated graph partitioning approaches that address these issues. These methods reduce the computational complexity by operating on supervoxels instead of voxels, incorporating features capable of describing the 3D shape of the target objects and using structured models to account for correlation in output variables. One of the non-trivial issues with such models is that their parameters must be carefully chosen for optimal performance. A popular approach to learning model parameters is a maximum-margin approach called Structured SVM (SSVM) that provides optimality guarantees but also suffers from two main drawbacks. First, SSVM-based approaches are usually limited to linear kernels, since more powerful nonlinear kernels cause the learning to become prohibitively expensive. In this thesis, we introduce an approach to “kernelize” the features so that a linear SSVM framework can leverage the power of nonlinear kernels without incurring their high computational cost. Second, the optimality guarentees are violated for complex models with strong inter-relations between the output variables. We propose a new subgradient-based method that is more robust and leads to improved convergence properties and increased reliability. The different approaches presented in this thesis are applicable to both natural and medical images. They are able to segment mitochondria at a performance level close to that of a human annotator, and outperform state-of-the-art segmentation techniques while still benefiting from a low learning time

MEDICAL MACHINE INTELLIGENCE: DATA-EFFICIENCY AND KNOWLEDGE-AWARENESS

Traditional clinician diagnosis requires massive manual labor from experienced doctors, which is time-consuming and costly. Computer-aided systems are therefore proposed to reduce doctors’ efforts by using machines to automatically make diagnosis and treatment recommendations. The recent success in deep learning has largely advanced the field of computer-aided diagnosis by offering an avenue to deliver automated medical image analysis. Despite such progress, there remain several challenges towards medical machine intelligence, such as unsatisfactory performance regarding challenging small targets, insufficient training data, high annotation cost, the lack of domain-specific knowledge, etc. These challenges cultivate the need for developing data-efficient and knowledge-aware deep learning techniques which can generalize to different medical tasks without requiring intensive manual labeling efforts, and incorporate domain-specific knowledge in the learning process. In this thesis, we rethink the current progress of deep learning in medical image analysis, with a focus on the aforementioned challenges, and present different data-efficient and knowledge-aware deep learning approaches to address them accordingly. Firstly, we introduce coarse-to-fine mechanisms which use the prediction from the first (coarse) stage to shrink the input region for the second (fine) stage, to enhance the model performance especially for segmenting small challenging structures, such as the pancreas which occupies only a very small fraction (e.g., < 0.5%) of the entire CT volume. The method achieved the state-of-the-art result on the NIH pancreas segmentation dataset. Further extensions also demonstrated effectiveness for segmenting neoplasms such as pancreatic cysts or multiple organs. Secondly, we present a semi-supervised learning framework for medical image segmentation by leveraging both limited labeled data and abundant unlabeled data. Our learning method encourages the segmentation output to be consistent for the same input under different viewing conditions. More importantly, the outputs from different viewing directions are fused altogether to improve the quality of the target, which further enhances the overall performance. The comparison with fully-supervised methods on multi-organ segmentation confirms the effectiveness of this method. Thirdly, we discuss how to incorporate knowledge priors for multi-organ segmentation. Noticing that the abdominal organ sizes exhibit similar distributions across different cohorts, we propose to explicitly incorporate anatomical priors on abdominal organ sizes, guiding the training process with domain-specific knowledge. The approach achieves 84.97% on the MICCAI 2015 challenge “Multi-Atlas Labeling Beyond the Cranial Vault”, which significantly outperforms previous state-of-the-art even using fewer annotations. Lastly, by rethinking how radiologists interpret medical images, we identify one limitation for existing deep-learning-based works on detecting pancreatic ductal adenocarcinoma is the lack of knowledge integration from multi-phase images. Thereby, we introduce a dual-path network where different paths are connected for multi-phase information exchange, and an additional loss is added for removing view divergence. By effectively incorporating multi-phase information, the presented method shows superior performance than prior arts on this matter