MRI segmentation: Methods and applications

The notion of using context information for solving high-level vision and medical image segmentation problems has been increasingly realized in the field. However, how to learn an effective and efficient context model, together with an image appearance model, remains mostly unknown. The current literature using Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) often involves specific algorithm design in which the modeling and computing stages are studied in isolation. In this paper, we propose a learning algorithm, auto-context. Given a set of training images and their corresponding label maps, we first learn a classifier on local image patches. The discriminative probability (or classification confidence) maps created by the learned classifier are then used as context information, in addition to the original image patches, to train a new classifier. The algorithm then iterates until convergence. Auto-context integrates low-level and context information by fusing a large number of low-level appearance features with context and implicit shape information. The resulting discriminative algorithm is general and easy to implement. Under nearly the same parameter settings in training, we apply the algorithm to three challenging vision applications: foreground/background segregation, human body configuration estimation, and scene region labeling. Moreover, context also plays a very important role in medical/brain images where the anatomical structures are mostly constrained to relatively fixed positions. With only some slight changes resulting from using 3D instead of 2D features, the auto-context algorithm applied to brain MRI image segmentation is shown to outperform state-of-the-art algorithms specifically designed for this domain. Furthermore, the scope of the proposed algorithm goes beyond image analysis and it has the potential to be used for a wide variety of problems for structured prediction problems.

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Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation

A system that automatically segments and labels glioblastoma-multiforme tumors in magnetic resonance images (MRIs) of the human brain is presented. The MRIs consist of T1-weighted, proton density, and T2-weighted feature images and are processed by a system which integrates knowledge-based (KB) techniques with multispectral analysis. Initial segmentation is performed by an unsupervised clustering algorithm. The segmented image, along with cluster centers for each class are provided to a rule-based expert system which extracts the intracranial region. Multispectral histogram analysis separates suspected tumor from the rest of the intracranial region, with region analysis used in performing the final tumor labeling. This system has been trained on three volume data sets and tested on thirteen unseen volume data sets acquired from a single MRI system. The KB tumor segmentation was compared with supervised, radiologist-labeled "ground truth" tumor volumes and supervised K-nearest neighbors tumor segmentations. The results of this system generally correspond well to ground truth, both on a per slice basis and more importantly in tracking total tumor volume during treatment over time.

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Automatic tumor segmentation using knowledge-based techniques

A robust fully automatic method for segmenting the brain from head magnetic resonance (MR) images has been developed, which works even in the presence of radio frequency (RF) inhomogeneities. It has been successful in segmenting the brain in every slice from head images acquired from several different MRI scanners, using different-resolution images and different echo sequences. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and "snakes" contouring techniques, and a priori knowledge, which is used to remove the eyes, which are tricky to remove based on image intensity alone. It is a multistage process, involving first removal of the background noise leaving a head mask, then finding a rough outline of the brain, then refinement of the rough brain outline to a final mask. The paper describes the main features of the method, and gives results for some brain studies.

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Fully automatic segmentation of the brain in MRI

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.

MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

Presents a knowledge-based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of the human brain. The system consists of 2 components: an unsupervised clustering algorithm and an expert system. MR brain data is initially segmented by the unsupervised algorithm, then the expert system locates a landmark tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The landmark tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. The knowledge base contains information on cluster distribution in feature space and tissue models. Since tissue shapes are irregular, their models and matching are specially designed: 1) qualitative tissue models are defined for brain tissues such as white matter; 2) default reasoning is used to match a model with an MR image; that is, if there is no mismatch between a model and an image, they are taken as matched. The system has been tested with 53 slices of MR images acquired at different times by 2 different scanners. It accurately identifies abnormal slices and provides a partial labeling of the tissues. It provides an accurate complete labeling of all normal tissues in the absence of large amounts of data nonuniformity, as verified by radiologists. Thus the system can be used to provide automatic screening of slices for abnormality. It also provides a first step toward the complete description of abnormal images for use in automatic tumor volume determination. >

Knowledge-based classification and tissue labeling of MR images of human brain

This paper presents a knowledge based approach to automatic classification and tissue labeling of 2D magnetic resonance (MR) images of human brain. The system consists of two components: an unsupervised clustering algorithm and an expert system. MR brain data is initially segmented by the unsupervised algorithm, then, the expert system locates a focus-of- attention tissue or cluster and analyzes it by matching it with a model or searching in it for an expected feature. The focus-of-attention tissue location and its analysis are repeated until a tumor is found or all tissues are labeled. Abnormal slices are labeled by reclustering regions of interest with knowledge accumulated from previous analysis. The domain knowledge contains tissue distribution in feature space acquired with a clustering algorithm, and tissue models. Default reasoning is used to match a qualitative model with its instances. The system has been tested with fifty-three slices acquired at different times by two different scanners.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Knowledge-based classification and tissue labeling of magnetic resonance images of human brain

This paper presents a novel multiparadigm segmentation method based upon knowledge based clustering with reclustering. The techniques described enhance unsupervised classification and achieve pattern labeling. First domain knowledge is utilized to decide where and how a clustering algorithm is applied, then clustering is iteratively applied to focus-of-attention patterns with the knowledge of how many expected classes there are in those patterns and the prototypical patterns of a class. Examples showing clustering improvements are given from brain MRI's and satellite images.

Knowledge based (re-)clustering

Segmentation has emerged as a fundamental field of computer vision and natural language processing, which assigns a label to every pixel/feature to extract regions of interest from an image/text. To evaluate the performance of segmentation, the Dice and IoU metrics are used to measure the degree of overlap between the ground truth and the predicted segmentation. In this paper, we establish a theoretical foundation of segmentation with respect to the Dice/IoU metrics, including the Bayes rule and Dice-/IoU-calibration, analogous to classification-calibration or Fisher consistency in classification. We prove that the existing thresholding-based framework with most operating losses are not consistent with respect to the Dice/IoU metrics, and thus may lead to a suboptimal solution. To address this pitfall, we propose a novel consistent ranking-based framework, namely RankDice/RankIoU, inspired by plug-in rules of the Bayes segmentation rule. Three numerical algorithms with GPU parallel execution are developed to implement the proposed framework in large-scale and high-dimensional segmentation. We study statistical properties of the proposed framework. We show it is Dice-/IoU-calibrated, and its excess risk bounds and the rate of convergence are also provided. The numerical effectiveness of RankDice/mRankDice is demonstrated in various simulated examples and Fine-annotated CityScapes, Pascal VOC and Kvasir-SEG datasets with state-of-the-art deep learning architectures.

Chunlin Li

Papers

Knowledge-based classification and tissue labeling of MR images of human brain

Knowledge-based classification and tissue labeling of magnetic resonance images of human brain

Knowledge based (re-)clustering

RankSEG: A Consistent Ranking-based Framework for Segmentation