<title>Three-dimensional active net for volume extraction</title>

3D Active Net is an energy-minimizing surface model which can extract a volume around features of interest from 3D volume data. It is deformable and evolves in 3D space to be attracted to salient features, according to its internal and image energy. The net can be fitted to the contours of a target by defining the image energy suitable for the contour property. We present test results of the extraction of a muscle from Visible Human Data by two methods : manual segmentation and the application of 3D Active Net. We use principal component analysis, which utilizes color information of the 3D volume data to define an ill-defined contour of the muscle, and then we use 3D Active Net. We recognize that the extracted object has a smooth and natural contour which is in contrast with that of a comparable manual segmentation, proving the advantage of our approach.

Background removal is a critical step in Visible Human Data (VHD) processing, which is the basic of all other researches. In this paper, a new segmentation algorithm based on the hybrid method for VHD background removal has been proposed, which combines a feature based segmentation method with a contour based one. The algorithm first determines the background part and the interested parts of an image at a coarse level by using its colour features, and then obtains a fine segmentation by using a Gradient Vector Flow (GVF) Snake model on the previous initial contour. Our test results on Chinese VHD show that the new algorithm is more robust and accurate than the previous methods.

The four-dimensional (4-D) NCAT phantom was developed to realistically model human anatomy based on the visible human data and cardiac and respiratory motions based on 4-D tagged magnetic resonance imaging and respiratory-gated CT data from normal human subjects. Currently, the 4-D NCAT phantom does not include the airway tree or its motion within the lungs. Also, each lung is defined with a single surface; the individual lobes are not distinguished. The authors further the development of the phantom by creating dynamic models for the individual lung lobes and for the airway tree in each lobe. NURBS surfaces for the lobes and an initial airway tree model (/spl sim/ 4 generations) were created through manual segmentation of the visible human data. A mathematical algorithm with physiological constraints was used to extend the original airway model to fill each lobe. For each parent airway branch inside a lobe, the algorithm extends the airway tree by creating two daughter branches modeled with cylindrical tubes. Parameters for the cylindrical tubes such as diameter, length, and angle are constrained based on flow parameters and available lung space.

The U S. National Library of Medicine (NLM) has long been a world leader in the archiving and distribution of the print-based images of biology and medicine. NLM has also been a pioneer in the use of computer systems to encode and distribute textual knowledge of the life sciences. NLM's Long Range Planning effort of 1985-86 foresaw a coming era where NLM's Bibliographic and factual database services would be complemented by libraries of digital images, distributed over high speed computer networks. The NLM Planning Panel on Electronic Imaging recommended that NLM should undertake the building a digital image library consisting of computerized tomography (CT) and magnetic resonance (MR) images, and cryosection images of a representative, carefully selected and prepared male and female cadaver -- the “Visible Human Project®.” The male and female Visible Human data sets are now being made available through a license agreement with the NLM. The data sets are supporting a wide range of educational, diagnostic, treatment planning, and commercial uses. The NLM, in partnership with other U.S. government research agencies has begun a three prong effort within the Visible Human Project to address: the creation of a new online, interactive, digital head-and-neck atlas; the development of a tool kit of computational programs capable of automatically performing many of the basic data handling functions required for using Visible Human data in applications; and the improved resolution of future Visible Human data sets through the reduction of the anatomical artifacts introduced by the methods used to stabilize and section the anatomical materials and the development of staining and wide-spectrum methods for increasing tissue contrast.

Non-rigid object tracking is an important task in computer vision, while its natural contour extraction is one of the most difficult problems during the process. Most tracking-by-detection methods are based on rectangular bounding-boxes, this will lead errors into subsequent detection. This paper present a novel superpixel-based detector for accurate natural contour extraction, there are three main contributions: 1) combining real-time superpixel segmentation with natural contour detection, 2) proposing an object-oriented natural contour extraction method for non-rigid objects, 3) proposing a non-rigid object detection method based on flexible scanning window. Compared with those bounding-box based detection methods, our detector can provide very accurate initial input of object model, then produce accurate natural contour output of the non-rigid object. Our detector broke the conventional detection method based on scanning rectangle, which greatly reduced the interference caused by background information. The experiments show that the proposed method outperforms the state-of-the-art algorithms not only on the contour accuracy but also on the computation cost. In addition, the initialization stage of our method overcomes the limitation of HT caused by the size of initial bounding-box.

Visible human data has been widely used in various medical research and computer science applications. We present a new application for this data: a method to classify which body segment a transverse cross section image belongs to. The labeling of the data is created with the guidance of an online body cross section tutorial. The visual properties of the images are represented using a variety of feature descriptors. To avoid problems that arise from the large dimensionality of features, feature selection is applied. The multi-class SVM is employed as the classifier. Both the CT scans and the color photographs of cryosections of the whole body (male and female) are used to test the proposed method. The high performance with overall accuracy above 98% on both the 2160 CT dataset and the 1870 cryosectional photos show the method is very promising. Because of its observed effectiveness on visible human data, we will extend our approach to classify figures in biomedical articles.

Intrabody communication (IBC) is a promising approach for realizing information transmission among various wearable or implantable devices within bodyNET. The existing IBC studies are limited to the local or abstract geometric channel model, which is limited to providing efficient channel characteristics for the various transmitting scenarios. The aim of this article is to reconstruct a whole-body model with anatomical features and combine the field-circuit model for investigating the human channel characteristics. A visible human data (VHD) is established by segmentation and 3-D reconstruction of layered tissues. The transmission attenuation was simulated and analyzed through the finite element method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In vivo</i> experiments and phantom experiments were implemented to validate the simulation results. For the on-body communication, the transmission attenuation of the short distance was similar to the low-pass filter’s amplitude-frequency characteristics. For the implantable communication, the consistency coefficient of the attenuation between in-body to on-body and on-body to in-body was higher than 98%, indicating that the body channel is bidirectional. This work provides a repeatable and verified VHD-based whole-body model for the human channel attenuation investigation. It is an essential step for the further development of IBC, such as the transceiver design and the multinodes’ protocol for bodyNET.

Current methods for estimating and reporting radiation dose from CT examinations are largely patient-generic; the body size and hence dose variation from patient to patient is not reflected. Furthermore, the current protocol designs rely on dose as a surrogate for the risk of cancer incidence, neglecting the strong dependence of risk on age and gender. The purpose of this study was to develop a method for estimating patient-specific radiation dose and cancer risk from CT examinations. The study included two patients (a 5-week-old female patient and a 12-year-old male patient), who underwent 64-slice CT examinations (LightSpeed VCT, GE Healthcare) of the chest, abdomen, and pelvis at our institution in 2006. For each patient, a nonuniform rational B-spine (NURBS) based full-body computer model was created based on the patient's clinical CT data. Large organs and structures inside the image volume were individually segmented and modeled. Other organs were created by transforming an existing adult male or female full-body computer model (developed from visible human data) to match the framework defined by the segmented organs, referencing the organ volume and anthropometry data in ICRP Publication 89. A Monte Carlo program previously developed and validated for dose simulation on the LightSpeed VCT scanner was used to estimate patient-specific organ dose, from which effective dose and risks of cancer incidence were derived. Patient-specific organ dose and effective dose were compared with patient-generic CT dose quantities in current clinical use: the volume-weighted CT dose index (CTDIvol) and the effective dose derived from the dose-length product (DLP). The effective dose for the CT examination of the newborn patient (5.7 mSv) was higher but comparable to that for the CT examination of the teenager patient (4.9 mSv) due to the size-based clinical CT protocols at our institution, which employ lower scan techniques for smaller patients. However, the overall risk of cancer incidence attributable to the CT examination was much higher for the newborn (2.4 in 1000) than for the teenager (0.7 in 1000). For the two pediatric-aged patients in our study, CTDIvol underestimated dose to large organs in the scan coverage by 30%-48%. The effective dose derived from DLP using published conversion coefficients differed from that calculated using patient-specific organ dose values by -57% to 13%, when the tissue weighting factors of ICRP 60 were used, and by -63% to 28%, when the tissue weighting factors of ICRP 103 were used. It is possible to estimate patient-specific radiation dose and cancer risk from CT examinations by combining a validated Monte Carlo program with patient-specific anatomical models that are derived from the patients' clinical CT data and supplemented by transformed models of reference adults. With the construction of a large library of patient-specific computer models encompassing patients of all ages and weight percentiles, dose and risk can be estimated for any patient prior to or after a CT examination. Such information may aid in decisions for image utilization and can further guide the design and optimization of CT technologies and scan protocols.

Objective. A major challenge in treatment of tumors near skeletal muscle is defining the target volume for suspected tumor invasion into the muscle. This study develops a framework that generates radiation target volumes with muscle fiber orientation directly integrated into their definition. The framework is applied to nineteen sacral tumor patients with suspected infiltration into surrounding muscles. Approach. To compensate for the poor soft-tissue contrast of CT images, muscle fiber orientation is derived from cryo-images of two cadavers from the human visible project (VHP). The approach consists of (a) detecting image gradients in the cadaver images representative of muscle fibers, (b) mapping this information onto the patient image, and (c) embedding the muscle fiber orientation into an expansion method to generate patient-specific clinical target volumes (CTV). The validation tested the consistency of image gradient orientation across VHP subjects for the piriformis, gluteus maximus, paraspinal, gluteus medius, and gluteus minimus muscles. The model robustness was analyzed by comparing CTVs generated using different VHP subjects. The difference in shape between the new CTVs and standard CTV was analyzed for clinical impact. Main results. Good agreement was found between the image gradient orientation across VHP subjects, as the voxel-wise median cosine similarity was at least 0.86 (for the gluteus minimus) and up to 0.98 for the piriformis. The volume and surface similarity between the CTVs generating from different VHP subjects was on average at least 0.95 and 5.13 mm for the Dice Similarity Coefficient and the Hausdorff 95% Percentile Index, showing excellent robustness. Finally, compared to the standard CTV with different margins in muscle and non-muscle tissue, the new CTV margins are reduced in muscle tissue depending on the chosen clinical margins. Significance. This study implements a method to integrate muscle fiber orientation into the target volume without the need for additional imaging.

The structures of superior mediastinum and their spatial relationships are complex and difficult to master. This study aimed to compare visualization of the superior mediastinum based on computed tomography (CT) images and on the thin sections of the Chinese visible human (CVH) data set to provide a sectional anatomical basis for diagnostic imaging of superior mediastinal pathology. CVH sections of the mediastinum of a 35-year old male were compared with plain and enhanced CT images of a 45-year old male without apparent abnormalities in the upper chest. In addition, a three-dimensional model based on the CVH sections was compared with a model based on CT images. Although CT imaging is noninvasive and can be carried out in many individuals, its weakness is clearly the visualization of small soft tissue structures. In this respect, the sectional anatomical approach of the CVH images is complementary, as it visualizes these small soft tissue structures due to the higher resolution in the plain of sectioning and the color of the different structures in the section. Three-dimensional surface and volume rendering of reconstructions of the CVH data set can help medical students and less experienced thoracic surgeons to familiarize themselves with the topographic anatomy of the superior mediastinal structures and their spatial relationships, and thus with interpreting CT images of patients.

This study is developed in the framework of RéCaMiA, a French regional (Rhône‐Alpes / Auvergne) microscopy network, which offers facilities through a panel of microscopes such as SEM, TEM, FIB, AFM, Confocal. The objective of the study is to compare results obtained with different microscopy techniques currently employed in three‐dimensional imaging, but it focuses more specifically on polymers [1‐3]. For the purpose a blend of polystyrene / acrylonitrile butadiene styrene (PS/ABS) is chosen. This material allows to obtain good contrast in SEM or TEM; microstructural features exist at the submicronic and nano scales; and its glass transition temperature T g is above 50°C. Two samples are stained by contact with a 4% osmium tetraoxide (OsO 4 ) water solution to reveal contrasts. 2 FIB–SEM (FEI Helios600, Zeiss NVisio40 dual beam), 2 TEM (JEOL JEM 1400, JEOL 2100F), 2 SEM (FEI ESEM XL30, FEI Quanta250) are used to study the 3D structure of the polymer. This study develops a multiscale approach. The tools used require 2 different procedures to get a 3D image: (a) to acquire a stack of individual imaging planes and to transform it into the corresponding 3D data volume or (b) to perform electron tomography. In the first case individual images are obtained through a slice and view approach in FIB, or through individual observations, in SEM or TEM, of several thin foils prepared by ultramicrotomy method at room temperature (serial sectioning). Backscattered electron images are collected. The 3D reconstructions of the polymer obtained by the different tools are compared each other. Whatever the approach used, the morphology “salami” shape is observed in the structure of the polymer. Structure parameters, such as size distribution of the nodules and the anisotropy coefficient, are extracted and compared. Different test parameters effects are investigated, the preparation of the sample (compression of the microstructure more or less), the voltage, the spacing between the individual imaging planes, etc. The balance between the reconstructed volume size, the voxel resolution, the total time to get the 3D data volume, the advantages and limitations of the methods used in this multiscale approach will also be discussed.

VR simulated training for less invasive vascular intervention

Exploded views and cutaway views have been demonstrated to be powerful visualization techniques to explore the complex internal structures in 3D volume data. Current approaches for creating them are mainly based on algorithms with very limited user controls. This paper proposes a novel set of whole-hand multitouch gestures for interactive creation and manipulation of these views on 3D volume data. Moreover, users can also interactively perform bimanual ‘V’-shape cut and volume exploding, remove unwanted volume partitions to effectively create cutaway views, and create threshold-based volume segmentation and exploded views. Our proposed interaction design was also tried out by eight participants in a preliminary pilot study.

"Patient specific dosimetry established during the last decade in modern radio-therapy. Usually, tracer kinetics in main compartments of observed metabolism is assessed from anterior and posterior whole body scans. The effective doses for each organ, derived by the MIRD scheme, provide evidence for following radiotherapeutic treatment and helps to meet vital dose limits for critical organs, e.g. kidneys. The calculation of individual dose in a three-dimensional context leads to more accurate dose estimates, as was proven by intensive research, but is still on the cusp to clinical application. In this work, a statistical approach, based on multi-modal image and feature data, is presented, to overcome manual segmentation, the most time consuming step, in 3D based dose calculation. 3D data volumes from a hybrid SPECT study, comprising SPECT and CT data, covering main compartments of metabolism, build the image features of a Gaussian classifier. From prior segmentations organspecific membership maps are derived, and substituted as additional feature into the segmentation procedure. Centroids, eccentricity and principal axes of organ models are registered to a rough thresholded image of the SPECT component, and define membership coefficients of the voxels. The new approach yields accurate results, even with real patient data. The new method needs minimal user interaction during selection of some sample regions, thus showing high potential for implementation in a clinical workflow."

To examine whether the third-trimester fetus can be assessed sonographically using 3-dimensional (3D) volume data sets. Twenty-seven consecutive third-trimester fetuses were evaluated. Fetuses were scanned using 2-dimensional (2D) imaging followed by 5 3D volume acquisitions. The initial scan was interpreted and reported based on the 2D images. The 3D volume data sets were independently reviewed offline several weeks later by 2 sonologists. Parameters evaluated included fetal presentation, placental location, amniotic fluid volume, fetal biometry including a calculation of estimated fetal weight, and major fetal anatomic structures. The result of the interpretation via 3D reconstruction of the volume from each of the 2 sonologists was compared with the original 2D sonography report. Fetal presentation, amniotic fluid volume, and placental location with respect to the cervix were correctly identified 100% of the time by each sonologist. The estimated fetal weight was within 10% of the 2D estimate 89% (95% CI, 0.71-0.98) of the time for sonologist A and 96% (95% CI, 0.81-0.99) of the time for sonologist B. The majority of major anatomic landmarks were adequately seen by both sonologists. Offline review of 3D volume data sets is a reliable method for determining fetal presentation, amniotic fluid volume, placental location, and estimating fetal weight in the third trimester.