Vascular structures are important information for education purpose, surgical planning and analysis. Extraction of blood vessels of the organ is a challenging task in the area of medical image processing and it is the first step before obtaining the structure. It is difficult to get accurate vessel segmentation results even with manually labeling by human being. The difficulty of vessels segmentation is the complicated structure of blood vessels and its large variations that make them hard to recognize. In this paper, we present deep artificial neural network architecture to automatically segment the vessels from computed tomography (CT) image. We proposed deep neural network (DNN) architecture for vessel segmentation from a medical CT volume, which consists of multi deep convolution neural networks to extract features from difference planes of CT data. Due to the problem of varies constrains that we cannot control, we add normalization process to make sure our network will well perform on clinical data. To validate effectiveness and efficiency of our proposed method, we conduct experiments on 20 clinical CT volumes. Our network can yield an average dice coefficient 0.879 on clinical data which better than state-of-the-art methods such as level set, Frangi, and submodular graph cuts.
The challenge of segmenting neurospheres (NSPs) from brightfield images includes uneven background illumination (vignetting), low contrast and shadow-casting appearance near the well wall. We propose a pipeline for neurosphere segmentation in brightfield images, focusing on shadow-casting removal. Firstly, we remove vignetting by creating a synthetic blank field image from a set of brightfield images of the whole well. Then, radial line integration is proposed to remove the shadow-casting and therefore facilitate automatic segmentation. Furthermore, a weighted bi-directional decay function is introduced to prevent undesired gradient effect of line integration on NSPs without shadow-casting. Afterward, multiscale Laplacian of Gaussian (LoG) and localized region-based level set are used to detect the NSP boundaries. Experimental results show that our proposed radial line integration method (RLI) achieves higher detection accuracy over existing methods in terms of precision, recall and F-score with less computational time.
A data clustering based vessel segmentation method is proposed for automatic liver vasculature segmentation
in CT images. It consists of a novel similarity measure which incorporates the spatial context, vesselness information
and line-direction information in a unique way. By combining the line-direction information and spatial
information into the data clustering process, the proposed method is able to take care of the fine details of the
vessel tree and suppress the image noise and artifacts at the same time. The proposed algorithm has been evaluated
on the real clinical contrast-enhanced CT images, and achieved excellent segmentation accuracy without
any experimentally set parameters.
Robust and efficient segmentation tools are important for the quantification of 3D liver and liver tumor volumes which
can greatly help clinicians in clinical decision-making and treatment planning. A two-module image analysis procedure
which integrates two novel semi-automatic algorithms has been developed to segment 3D liver and liver tumors from
multi-detector computed tomography (MDCT) images. The first module is to segment the liver volume using a flippingfree
mesh deformation model. In each iteration, before mesh deformation, the algorithm detects and avoids possible
flippings which will cause the self-intersection of the mesh and then the undesired segmentation results. After flipping
avoidance, Laplacian mesh deformation is performed with various constraints in geometry and shape smoothness. In the
second module, the segmented liver volume is used as the ROI and liver tumors are segmented by using support vector
machines (SVMs)-based voxel classification and propagational learning. First a SVM classifier was trained to extract
tumor region from one single 2D slice in the intermediate part of a tumor by voxel classification. Then the extracted
tumor contour, after some morphological operations, was projected to its neighboring slices for automated sampling,
learning and further voxel classification in neighboring slices. This propagation procedure continued till all tumorcontaining
slices were processed. The performance of the whole procedure was tested using 20 MDCT data sets and the
results were promising: Nineteen liver volumes were successfully segmented out, with the mean relative absolute volume
difference (RAVD), volume overlap error (VOE) and average symmetric surface distance (ASSD) to reference
segmentation of 7.1%, 12.3% and 2.5 mm, respectively. For live tumors segmentation, the median RAVD, VOE and
ASSD were 7.3%, 18.4%, 1.7 mm, respectively.
In recent years more and more computer aided diagnosis (CAD) systems are being used routinely in hospitals. Image-based
knowledge discovery plays important roles in many CAD applications, which have great potential to be integrated
into the next-generation picture archiving and communication systems (PACS). Robust medical image segmentation
tools are essentials for such discovery in many CAD applications. In this paper we present a platform with necessary
tools for performance benchmarking for algorithms of liver segmentation and volume estimation used for liver
transplantation planning. It includes an abdominal computer tomography (CT) image database (DB), annotation tools, a
ground truth DB, and performance measure protocols. The proposed architecture is generic and can be used for other
organs and imaging modalities. In the current study, approximately 70 sets of abdominal CT images with normal livers
have been collected and a user-friendly annotation tool is developed to generate ground truth data for a variety of organs,
including 2D contours of liver, two kidneys, spleen, aorta and spinal canal. Abdominal organ segmentation algorithms
using 2D atlases and 3D probabilistic atlases can be evaluated on the platform. Preliminary benchmark results from the
liver segmentation algorithms which make use of statistical knowledge extracted from the abdominal CT image DB are
also reported. We target to increase the CT scans to about 300 sets in the near future and plan to make the DBs built
available to medical imaging research community for performance benchmarking of liver segmentation algorithms.
Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.