The domain knowledge based graph-cut model for liver CT segmentation

In this paper, we present a semi-supervised approach for liver segmentation from computed tomography (CT) scans, which is based on the graph cut model integrated with domain knowledge. Firstly, some hard constraints are obtained according to the knowledge of liver characteristic appearance and anatomical location. Secondly, the energy function is constructed via knowledge based similarity measure. A path-based spatial connectivity measure is applied for robust regional properties. Finally, the image is interpreted as a graph, afterwards the segmentation problem is casted as an optimal cut on it, which can be computed through the existing max-flow algorithm. The model is evaluated on MICCAI 2007 liver segmentation challenge datasets and some other CT volumes from the hospital. The experimental results show its effectiveness and efficiency.     

Anatomical atlas in the context of head and neck radiotherapy and its use to automatic segmentation

In this paper we present a methodology to form an anatomical atlas based on the analysis of dense deformation fields recovered by the Morphons non-rigid registration algorithm. The methodology is based on measuring the bending energy required to register the whole database to a reference, and the atlas is the one image in the database which yields the smallest bending energy when taken as reference. The suitability of our atlas is demonstrated in the context of head and neck radiotherapy through its application to a database with thirty-one computed tomography (CT) images of the head and neck region. In head and neck radiotherapy, CT is the most frequently used modality for the segmentation of organs at risk and clinical target volumes. One challenge brought by the use of CT images is the presence of important artifacts caused by dental implants. The presence of such artifacts hinders the use of intensity averages, thus severely limiting the application of most atlas building techniques described in the literature in this context. The results presented in the paper show that our bending energy model faithfully represents the shape variability of patients in the head and neck region; they also show its good performance in segmentation of volumes of interest in radiotherapy. Moreover, when compared to other atlases of similar performance in automatic segmentation, our atlas presents the desirable feature of not being blurred after intensity averaging.     

A hybrid graph-based approach for right ventricle segmentation in cardiac MRI by long axis information transition

Right ventricle segmentation is a challenging task in cardiac image analysis due to its complex anatomy and huge shape variations. In this paper, we proposed a semi-automatic approach by incorporating the right ventricle region and shape information into livewire framework and using one slice segmentation result for the segmentation of adjacent slices. The region term is created using our previously proposed region growing algorithm combined with the SUSAN edge detector while the shape prior is obtained by forming a signed distance function (SDF) from a set of binary masks of the right ventricle and applying PCA on them. Short axis slices are divided into two groups: primary and secondary slices. A primary slice is segmented by the proposed modified livewire and the livewire seeds are transited to a pre-processed version of upper and lower slices (secondary) to find new seed positions in these slices. The shortest path algorithm is applied on each pair of seeds for segmentation. This method is applied on 48 MR patients (from MICCAI’12 Right Ventricle Segmentation Challenge) and yielded an average Dice Metric of 0.937 ± 0.58 and the Hausdorff Distance of 5.16 ± 2.88 mm for endocardium segmentation. The correlation with the ground truth contours were measured as 0.99, 0.98, and 0.93 for EDV, ESV and EF respectively. The qualitative and quantitative results declare that the proposed method outperforms the state-of-the-art methods that uses the same dataset and the cardiac global functional parameters are calculated robustly by the proposed method.     

Prostate segmentation on T2 MRI using Optimal Surface Detection

This paper deals with a T2 MRI prostate segmentation method. We assume to have an initial surface mesh obtained interactively or after a first rough segmentation. The surface of the prostate is then searched within the initial mesh neighborhood using the Optimal Surface Detection algorithm (OSD). This algorithm is based on the construction of a directed graph from the information obtained around the initial mesh. The optimal surface is then obtained by a graph cut. Three different cost functions for the graph have been explored, one based on the local gradient, another on a statistical model of shape and a third on a model of gradient profile. The parameters of this method have been tuned on 33 different T2 MRI volumes.     

Liver vessel segmentation based on extreme learning machine

Liver-vessel segmentation plays an important role in vessel structure analysis for liver surgical planning. This paper presents a liver-vessel segmentation method based on extreme learning machine (ELM). Firstly, an anisotropic filter is used to remove noise while preserving vessel boundaries from the original computer tomography (CT) images. Then, based on the knowledge of prior shapes and geometrical structures, three classical vessel filters including Sato, Frangi and offset medialness filters together with the strain energy filter are used to extract vessel structure features. Finally, the ELM is applied to segment liver vessels from background voxels. Experimental results show that the proposed method can effectively segment liver vessels from abdominal CT images, and achieves good accuracy, sensitivity and specificity.     

Volume interpolation of CT images from tree trunks

Computerized tomography as a non-destructive scanning method to analyze wood structures has become an important technique in tree research. The possibility to reconstruct three-dimensional volumes based on a number of slices of two-dimensional data from CT scans is strongly dependent on the number of measured slices. Radial basis function methods can be successfully used to interpolate CT images with the aim of obtaining a satisfactory reconstruction of tree trunks. In contrast to standard interpolation techniques, our method takes into account that wood structures differ more in the radial than in the longitudinal direction. Therefore we obtain better interpolation results for wood structures.     

A 3D Network Based Shape Prior for Automatic Myocardial Disease Segmentation in Delayed-Enhancement MRI

Objectives: In this work, a new deep learning model for relevant myocardial infarction segmentation from Late Gadolinium Enhancement (LGE)-MRI is proposed. Moreover, our novel segmentation method aims to detect microvascular-obstructed regions accurately. Material and methods: We first segment the anatomical structures, i.e., the left ventricular cavity and the myocardium, to achieve a preliminary segmentation. Then, a shape prior based framework that fuses the 3D U-Net architecture with 3D Autoencoder segmentation framework to constrain the segmentation process of pathological tissues is applied. Results: The proposed network reached outstanding myocardial segmentation compared with the human-level performance with the average Dice score of ‘0.9507’ for myocardium, ‘0.7656’ for scar, and ‘0.8377’ for MVO on the validation set consisting of 16 DE-MRI volumes selected from the training EMIDEC dataset. Conclusion: It is concluded that our approach's extensive validation and comprehensive comparison against existing state-of-the-art deep learning models on three annotated datasets, including healthy and diseased exams, make this proposal a reliable tool to enhance MI diagnosis.     

Measuring egg size using digital photography: testing Hoyt's method using Florida Scrub-Jay eggs

ABSTRACT.   Egg volumes are most often estimated using a mathematical model that incorporates length and width measurements and a species-specific shape variable. Although adequate in many respects, this technique does not account for intraspecific variation in egg shape. We developed a computer-automated technique that uses calibrated digital photographs to render precise measurements of several egg-size parameters including length, width, volume, and surface area. The system extracts egg outlines from photographs, and divides each egg into latitudinal slices that are subsequently regarded as simple geometric shapes (cylinders or cone frustra) with volumes and surface areas that can be summed to generate size parameters for the entire egg. We tested this technique using 491 eggs from Florida Scrub-Jay ( Aphelocoma coerulescens ) nests and compared the resulting egg volumes with volumes calculated using the preeminent method of estimating volume from linear measurements. Our method was highly accurate, and differences between the volumes from our method and the alternative method were strongly associated with variation in egg shape. Advantages of our technique include decreased handling of eggs and increased accuracy. Software resources and additional information regarding the technique are available at http://www.archbold-station.org/abs/data/birddata/Bridge-JFO-eggsize.htm .     

Application of a semi-automatic cartilage segmentation method for biomechanical modeling of the knee joint

Manual segmentation of articular cartilage from knee joint 3D magnetic resonance images (MRI) is a time consuming and laborious task. Thus, automatic methods are needed for faster and reproducible segmentations. In the present study, we developed a semi-automatic segmentation method based on radial intensity profiles to generate 3D geometries of knee joint cartilage which were then used in computational biomechanical models of the knee joint. Six healthy volunteers were imaged with a 3T MRI device and their knee cartilages were segmented both manually and semi-automatically. The values of cartilage thicknesses and volumes produced by these two methods were compared. Furthermore, the influences of possible geometrical differences on cartilage stresses and strains in the knee were evaluated with finite element modeling. The semi-automatic segmentation and 3D geometry construction of one knee joint (menisci, femoral and tibial cartilages) was approximately two times faster than with manual segmentation. Differences in cartilage thicknesses, volumes, contact pressures, stresses, and strains between segmentation methods in femoral and tibial cartilage were mostly insignificant (p > 0.05) and random, i.e. there were no systematic differences between the methods. In conclusion, the devised semi-automatic segmentation method is a quick and accurate way to determine cartilage geometries; it may become a valuable tool for biomechanical modeling applications with large patient groups.     

Medical image segmentation based on level set and isoperimetric constraint

Level set based methods are being increasingly used in image segmentation. In these methods, various shape constraints can be incorporated into the energy functionals to obtain the desired shapes of the contours represented by their zero level sets of functions. Motivated by the isoperimetric inequality in differential geometry, we propose a segmentation method in which the isoperimetric constrain is integrated into a level set framework to penalize the ratio of its squared perimeter to its enclosed area of an active contour. The new model can ensure the compactness of segmenting objects and complete missing or/and blurred parts of their boundaries simultaneously. The isoperimetric shape constraint is free of explicit expressions of shapes and scale-invariant. As a result, the proposed method can handle various objects with different scales and does not need to estimate parameters of shapes. Our method can segment lesions with blurred or/and partially missing boundaries in ultrasound, Computed Tomography (CT) and Magnetic Resonance (MR) images efficiently. Quantitative evaluation also confirms that the proposed method can provide more accurate segmentation than two well-known level set methods. Therefore, our proposed method shows potential of accurate segmentation of lesions for applying in diagnoses and surgical planning.     

Rotationally resliced 3D prostate segmentation of MR images using Bhattacharyya similarity and active band theory

PurposeIn this article, we propose a novel, semi-automatic segmentation method to process 3D MR images of the prostate using the Bhattacharyya coefficient and active band theory with the goal of providing technical support for computer-aided diagnosis and surgery of the prostate.MethodsOur method consecutively segments a stack of rotationally resectioned 2D slices of a prostate MR image by assessing the similarity of the shape and intensity distribution in neighboring slices. 2D segmentation is first performed on an initial slice by manually selecting several points on the prostate boundary, after which the segmentation results are propagated consecutively to neighboring slices. A framework of iterative graph cuts is used to optimize the energy function, which contains a global term for the Bhattacharyya coefficient with the help of an auxiliary function. Our method does not require previously segmented data for training or for building statistical models, and manual intervention can be applied flexibly and intuitively, indicating the potential utility of this method in the clinic.ResultsWe tested our method on 3D T2-weighted MR images from the ISBI dataset and PROMISE12 dataset of 129 patients, and the Dice similarity coefficients were 90.34 ± 2.21% and 89.32 ± 3.08%, respectively. The comparison was performed with several state-of-the-art methods, and the results demonstrate that the proposed method is robust and accurate, achieving similar or higher accuracy than other methods without requiring training.ConclusionThe proposed algorithm for segmenting 3D MR images of the prostate is accurate, robust, and readily applicable to a clinical environment for computer-aided surgery or diagnosis.     

Objective threshold selection procedure (OTS) for segmentation of scanning laser confocal microscope images

The determination of volumes and interface areas from confocal laser scanning microscopy (CLSM) images requires the identification of component objects by segmentation. An automated method for the determination of segmentation thresholds for CLSM imaging of biofilms was developed. The procedure, named objective threshold selection (OTS), is a three-dimensional development of the approach introduced by the popular robust automatic threshold selection (RATS) method. OTS is based on the statistical properties of local gray-values and gradients in the image. By characterizing the dependence between a volumetric feature and the intensity threshold used for image segmentation, the former can be determined with an arbitrary confidence level, with no need for user intervention. The identification of an objective segmentation procedure renders the possibility for the full automation of volume and interfacial area measurement. Images from two distinct biofilm systems, acquired using different experimental techniques and instrumental setups were segmented by OTS to determine biofilm volume and interfacial area. The reliability of measurements for each case was analyzed to identify optimal procedure for image acquisition. The automated OTS method was shown to reproduce values obtained manually by an experienced operator.     

Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net

Deep learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging. The aim of this work is to design and evaluate an algorithm capable of segmenting bones in dual-energy CT data sets. A convolutional neural network based on the 3D U-Net architecture was implemented and evaluated using high tube voltage images, mixed images and dual-energy images from 30 patients. The network performed well on all the data sets; the mean Dice coefficient for the test data was larger than 0.963. Of special interest is that it performed better on dual-energy CT volumes compared to mixed images that mimicked images taken at 120 kV. The corresponding increase in the Dice coefficient from 0.965 to 0.966 was small since the enhancements were mainly at the edges of the bones. The method can easily be extended to the segmentation of multi-energy CT data.     

Approche hybride combinant champs de Markov et modèle statistique de forme pour l’extraction des contours de la prostate en IRM

Recent progress in magnetic resonance imaging (MRI) has enabled new prostate cancer diagnosis techniques. The newest challenges in this field are to enhance image-based tumours detection. In such a context, the extraction of prostate's contours is a crucial step in the interpretation of MR images, and is usually carried out by an expert radiologist. This is though a tedious time consuming task, especially in 3D images (like CT and MRI). In addition, manual delineation is not reproducible because of differences between observers. In this paper, we introduce a novel method for automatic segmentation of prostate MRI that could help physicians in extracting 3D outlines of the gland. First a deformable shape model is used to obtain a first segmentation. The latter is refined using intensity information and Markov Random Fields modelling of regions. We use the Iterative Conditional Mode for optimising voxels’ labelling according to a Maximum A Posteriori criterion. Results from evaluation on patients’ data show that the method is satisfyingly accurate, fast and robust which makes it suitable for use in a clinical context. A multicentric validation and transfer to the industry would bring the contributions of this method to clinical routine and help improving diagnosis of prostate cancer.     

The use of sparse CT datasets for auto-generating accurate FE models of the femur and pelvis

The finite element (FE) method when coupled with computed tomography (CT) is a powerful tool in orthopaedic biomechanics. However, substantial data is required for patient-specific modelling. Here we present a new method for generating a FE model with a minimum amount of patient data. Our method uses high order cubic Hermite basis functions for mesh generation and least-square fits the mesh to the dataset. We have tested our method on seven patient data sets obtained from CT assisted osteodensitometry of the proximal femur. Using only 12 CT slices we generated smooth and accurate meshes of the proximal femur with a geometric root mean square (RMS) error of less than 1 mm and peak errors less than 8 mm. To model the complex geometry of the pelvis we developed a hybrid method which supplements sparse patient data with data from the visible human data set. We tested this method on three patient data sets, generating FE meshes of the pelvis using only 10 CT slices with an overall RMS error less than 3 mm. Although we have peak errors about 12 mm in these meshes, they occur relatively far from the region of interest (the acetabulum) and will have minimal effects on the performance of the model. Considering that linear meshes usually require about 70-100 pelvic CT slices (in axial mode) to generate FE models, our method has brought a significant data reduction to the automatic mesh generation step. The method, that is fully automated except for a semi-automatic bone/tissue boundary extraction part, will bring the benefits of FE methods to the clinical environment with much reduced radiation risks and data requirement.     

Multi-Scale and Shape Constrained Localized Region-Based Active Contour Segmentation of Uterine Fibroid Ultrasound Images in HIFU Therapy

Purpose

To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids.

Methods

We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results.

Results

Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model).

Conclusions

An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.     

Application of CT images in the diagnosis of lung cancer based on finite mixed model

ObjectiveInvestigating the application of CT images when diagnosing lung cancer based on finite mixture model is the objective. Method: 120 clean healthy rats were taken as the research objects to establish lung cancer rat model and carry out lung CT image examination. After the successful CT image data preprocessing, the image is segmented by different methods, which include lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization – Gaussian mixture model (APSO-GMM), lung nodule segmentation on the basis of Adaptive Particle Swarm Optimization – gamma mixture model (APSO-GaMM), lung nodule segmentation based on statistical information and self-selected mixed distribution model, and lung nodule segmentation based on neighborhood information and self-selected mixed distribution model. The segmentation effect is evaluated. Results: Compared with the results of lung nodule segmentation based on statistical information and self-selected mixed distribution model, the Dice coefficient of lung nodule segmentation based on neighborhood information and self-selected mixed distribution model is higher, the relative final measurement accuracy is smaller, the segmentation is more accurate, but the running time is longer. Compared with APSO-GMM and APSO-GaMM, the dice value of self-selected mixed distribution model segmentation method is larger, and the final measurement accuracy is smaller. Conclusion: Among the five methods, the dice value of the self-selected mixed distribution model based on neighborhood information is the largest, and the relative accuracy of the final measurement is the smallest, indicating that the segmentation effect of the self-selected mixed distribution model based on neighborhood information is the best.     

Multi-Atlas Based Adaptive Active Contour Model with Application to Organs at Risk Segmentation in Brain MR Images

BackgroundAccurate delineation of organs at risk (OARs) is critical in radiotherapy. Manual delineation is tedious and suffers from both interobserver and intraobserver variability. Automatic segmentation of brain MR images has a wide range of applications in brain tumor radiotherapy. In this paper, we propose a multi-atlas based adaptive active contour model for OAR automatic segmentation in brain MR images.MethodsThe proposed method consists of two parts: multi-atlas based OAR contour initiation and an adaptive edge and local region based active contour evolution. In the adaptive active contour model, we define an energy functional with an adaptive edge intensity fitting force which is responsible for evaluating contour inwards or outwards, and a local region intensity fitting force which guides the evolution of the contour.ResultsExperimental results show that the proposed method achieved more accurate segmentation results in brainstem, eyes and lens automatic segmentation with the Dice Similar Coefficient (DSC) value of 87.19%, 91.96%, 77.11% respectively. Besides, the dosimetric parameters also demonstrate the high consistency of the manual OAR delineations and the auto segmentation results of the proposed method in brain tumor radiotherapy.ConclusionsThe geometric and dosimetric evaluations show the desirable performance of the proposed method on the application of OARs segmentations in brain tumor radiotherapy.     

A luminescence probe for metallothionein in liver tissue: emission intensity measured directly from copper metallothionein induced in rat liver

We report the first use of an emission probe based on the Cu(I)-thiolate chromophore, for the direct observation of copper metallothionein located in samples of rat liver. Elevated synthesis of Cu-MT in the rat liver was induced by subcutaneous injections of a series of aqueous CuCl2 solutions containing increasing amounts of Cu(II). Luminescence intensity in the 600 nm region, detected from frozen solutions of Cu-MT and from slices of the liver frozen at 77 K, following excitation in the 300 nm region, was dependent on the concentration of the Cu(II) used in the inducing solution. No such luminescence intensity was found for control samples obtained from the livers of rats not exposed to copper salts. It is suggested that this new method will allow direct visualization of Cu-MT in tissue where genetic disorders impare copper metabolism.