Antong Chen

Micro-CT imaging: Developing criteria for examining fetal skeletons in regulatory developmental toxicology studies – A workshop report

During the past two decades the use and refinements of imaging modalities have markedly increased making it possible to image embryos and fetuses used in pivotal nonclinical studies submitted to regulatory agencies. Implementing these technologies into the Good Laboratory Practice environment requires rigorous testing, validation, and documentation to ensure the reproducibility of data. A workshop on current practices and regulatory requirements was held with the goal of defining minimal criteria for the proper implementation of these technologies and subsequent submission to regulatory agencies. Micro-computed tomography (micro-CT) is especially well suited for high-throughput evaluations, and is gaining popularity to evaluate fetal skeletons to assess the potential developmental toxicity of test agents. This workshop was convened to help scientists in the developmental toxicology field understand and apply micro-CT technology to nonclinical toxicology studies and facilitate the regulatory acceptance of imaging data. Presentations and workshop discussions covered: (1) principles of micro-CT fetal imaging; (2) concordance of findings with conventional skeletal evaluations; and (3) regulatory requirements for validating the system. Establishing these requirements for micro-CT examination can provide a path forward for laboratories considering implementing this technology and provide regulatory agencies with a basis to consider the acceptability of data generated via this technology.

Characterization of bone abnormalities from micro-CT images for evaluating drug toxicity in developmental and reproductive toxicology (DART) studies

Routinely, compounds are assessed by developmental and reproductive toxicology (DART) studies to evaluate the potential for drug-induced birth defects. High-throughput micro-CT images are being used to evaluate skeletal abnormalities due to its ability to provide high quality images of bone structures. Currently, these micro-CT images are visually inspected for skeletal abnormalities, which is a time and resource intensive process. To reduce the resources needed for skeletal evaluation, we developed image analysis strategies that allow for automatic segmentation of whole body CT images into individual bones and use structural variations of shape characteristics to classify bones as normal or abnormal. Extraction of various structures in the skull and torso were accomplished sequentially starting with skull bones and moving towards the neck, vertebrae, ribs, and limbs. A total of 17 skull bones/structures (supraoccipital, mandible, squamosals, zygomatics, etc.) and 20 torso structures (ribs, spine, humerus, femur, tibia, etc.) were identified and isolated using this algorithm. Next, we used geometrical (volume, length, width, etc.) and shape-based characteristics to identify bones lying outside the normal distribution of numbers, shapes and sizes to flag fetuses for potential abnormalities. We applied this tool to a test data set of 167 fetuses with verified skeletal abnormalities and received sensitivity of 0.959 and specificity of 0.805. This analysis platform allows for fully automated batch processing of images. Future work will include further development of the current platform to improve performance.

Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

High-throughput micro-CT imaging has been used in our laboratory to evaluate fetal skeletal morphology in developmental toxicology studies. Currently, the volume-rendered skeletal images are visually inspected and observed abnormalities are reported for compounds in development. To improve the efficiency and reduce human error of the evaluation, we implemented a framework to automate the evaluation process. The framework starts by dividing the skull into regions of interest and then measuring various geometrical characteristics. Normal/abnormal classification on the bone segments is performed based on identifying statistical outliers. In pilot experiments using rabbit fetal skulls, the majority of the skeletal abnormalities can be detected successfully in this manner. However, there are shape-based abnormalities that are relatively subtle and thereby difficult to identify using the geometrical features. To address this problem, we introduced a model-based approach and applied this strategy on the squamosal bone. We will provide details on this active shape model (ASM) strategy for the identification of squamosal abnormalities and show that this method improved the sensitivity of detecting squamosal-related abnormalities from 0.48 to 0.92.

Automatic pose correction for image-guided nonhuman primate brain surgery planning

Intracranial delivery of recombinant DNA and neurochemical analysis in nonhuman primate (NHP) requires precise targeting of various brain structures via imaging derived coordinates in stereotactic surgeries. To attain targeting precision, the surgical planning needs to be done on preoperative three dimensional (3D) CT and/or MR images, in which the animals head is fixed in a pose identical to the pose during the stereotactic surgery. The matching of the image to the pose in the stereotactic frame can be done manually by detecting key anatomical landmarks on the 3D MR and CT images such as ear canal and ear bar zero position. This is not only time intensive but also prone to error due to the varying initial poses in the images which affects both the landmark detection and rotation estimation. We have introduced a fast, reproducible, and semi-automatic method to detect the stereotactic coordinate system in the image and correct the pose. The method begins with a rigid registration of the subject images to an atlas and proceeds to detect the anatomical landmarks through a sequence of optimization, deformable and multimodal registration algorithms. The results showed similar precision (maximum difference of 1.71 in average in-plane rotation) to a manual pose correction.

Quantification of Cy-5 siRNA signal in the intra-vital multi-photon microscopy images

Transgenic mice with Tie2- green fluorescent protein (GFP) are used as a model to study the kinetic distribution of the Cy5-siRNA delivered by lipid nanoparticles (LNP) into the liver. After the mouse is injected with the LNP, it undergoes a procedure of intra-vital multi-photon microscopy imaging over a period of two hours, during which the process for the nanoparticle to diffuse into the hepatocytes from the vasculature system is monitored. Since the images are obtained in-vivo, the quantification of Cy5 kinetics suffers from the moving field of view (FOV). A method is proposed to register the sequence of images through template matching. Based on the semi-automatic segmentations of the vessels in the common FOV, the registered images are segmented into three regions of interest (ROI) in which the Cy5 signals are quantified. Computation of the percentage signal strength in the ROIs over time allows for the analysis of the diffusion of Cy5-siRNA into the hepatocytes, and helps demonstrate the effectiveness of the Cy5-siRNA delivery vehicle.

Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional LSTM.

In the development of treatments for cardiovascular diseases, short axis cardiac cine MRI is important for the assessment of various structural and functional properties of the heart. In short axis cardiac cine MRI, Cardiac properties including the ventricle dimensions, stroke volume, and ejection fraction can be extracted based on accurate segmentation of the left ventricle (LV) myocardium. One of the most advanced segmentation methods is based on fully convolutional neural networks (FCN) and can be successfully used to do segmentation in cardiac cine MRI slices. However, the temporal dependency between slices acquired at neighboring time points is not used. Here, based on our previously proposed FCN structure, we proposed a new algorithm to segment LV myocardium in porcine short axis cardiac cine MRI by incorporating convolutional long short-term memory (Conv-LSTM) to leverage the temporal dependency. In this approach, instead of processing each slice independently in a conventional CNN-based approach, the Conv-LSTM architecture captures the dynamics of cardiac motion over time. In a leave-one-out experiment on 8 porcine specimens (3,600 slices), the proposed approach was shown to be promising by achieving average mean Dice similarity coefficient (DSC) of 0.84, Hausdorff distance (HD) of 6.35 mm, and average perpendicular distance (APD) of 1.09 mm when compared with manual segmentations, which improved the performance of our previous FCN-based approach (average mean DSC=0.84, HD=6.78 mm, and APD=1.11 mm). Qualitatively, our model showed robustness against low image quality and complications in the surrounding anatomy due to its ability to capture the dynamics of cardiac motion.

Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

In developing treatment of cardiovascular diseases, short axis cine MRI has been used as a standard technique for understanding the global structural and functional characteristics of the heart, e.g. ventricle dimensions, stroke volume and ejection fraction. To conduct an accurate assessment, heart structures need to be segmented from the cine MRI images with high precision, which could be a laborious task when performed manually. Herein a fully automatic framework is proposed for the segmentation of the left ventricle from the slices of short axis cine MRI scans of porcine subjects using a deep learning approach. For training the deep learning models, which generally requires a large set of data, a public database of human cine MRI scans is used. Experiments on the 3150 cine slices of 7 porcine subjects have shown that when comparing the automatic and manual segmentations the mean slice-wise Dice coefficient is about 0.930, the point-to-curve error is 1.07 mm, and the mean slice-wise Hausdorff distance is around 3.70 mm, which demonstrates the accuracy and robustness of the proposed inter-species translational approach.

Transfer Learning for the Fully Automatic Segmentation of Left Ventricle Myocardium in Porcine Cardiac Cine MR Images

A fully automatic approach for the segmentation of the left ventricle (LV) myocardium in porcine cardiac cine MRI images is proposed based on deep convolutional neural networks (CNN). We trained a 56-layer residual learning CNN (ResNet-56) from scratch on a set of porcine cine MRI images acquired internally, and another CNN via transfer learning by fine tuning a network previously trained on a public human cine MRI dataset. A leave-one-out validation was performed on an 8-specimen porcine cardiac cine MRI dataset (3,600 slices). Comparisons with manual segmentations show that both CNN models are able to produce precise results (99.94% “good” segmentations), while the CNN trained through transfer learning performs better by achieving Dice similarity coefficient (DSC) of 0.86, Hausdorff distance (HD) of 4.01 mm, and overall average perpendicular distance (APD) of 1.04 mm on average.

Image-guided intracranial cannula placement for awake in vivo microdialysis in nonhuman primates

Intracranial microdialysis is used for sampling neurochemicals and large peptides along with their metabolites from the interstitial fluid (ISF) of the brain. The ability to perform this in nonhuman primates (NHP) e.g., rhesus could improve the prediction of pharmacokinetic (PK) and pharmacodynamics (PD) action of drugs in human. However, microdialysis in rhesus brains is not as routinely performed as in rodents. One challenge is that the precise intracranial probe placement in NHP brains is difficult due to the richness of the anatomical structure and the variability of the size and shape of brains across animals. Also, a repeatable and reproducible ISF sampling from the same animal is highly desirable when combined with cognitive behaviors or other longitudinal study end points. Toward that end, we have developed a semi-automatic flexible neurosurgical method employing MR and CT imaging to (a) derive coordinates for permanent guide cannula placement in mid-brain structures and (b) fabricate a customized recording chamber to implant above the skull for enclosing and safeguarding access to the cannula for repeated experiments. In order to place the intracranial guide cannula in each subject, the entry points in the skull and the depth in the brain were derived using co-registered images acquired from MR and CT scans. The anterior/posterior (A/P) and medial-lateral (M/L) rotation in the pose of the animal was corrected in the 3D image to appropriately represent the pose used in the stereotactic frame. An array of implanted fiducial markers was used to transform stereotactic coordinates to the images. The recording chamber was custom fabricated using computer-aided design (CAD), such that it would fit the contours of the individual skull with minimum error. The chamber also helped in guiding the cannula through the entry points down a trajectory into the depth of the brain. We have validated our method in four animals and our results indicate average placement error of cannula to be 1.20 ± 0.68 mm of the targeted positions. The approach employed here for derivation of the coordinates, surgical implantation and post implant validation is built using traditional access to surgical and imaging methods without the necessity of intra-operative imaging. The validation of our method lends support to its wider application in most nonhuman primate laboratories with onsite MR and CT imaging capabilities.

Targeting of deep-brain structures in nonhuman primates using MR and CT Images

In vivo gene delivery in central nervous systems of nonhuman primates (NHP) is an important approach for gene therapy and animal model development of human disease. To achieve a more accurate delivery of genetic probes, precise stereotactic targeting of brain structures is required. However, even with assistance from multi-modality 3D imaging techniques (e.g. MR and CT), the precision of targeting is often challenging due to difficulties in identification of deep brain structures, e.g. the striatum which consists of multiple substructures, and the nucleus basalis of meynert (NBM), which often lack clear boundaries to supporting anatomical landmarks. Here we demonstrate a 3D-image-based intracranial stereotactic approach applied toward reproducible intracranial targeting of bilateral NBM and striatum of rhesus. For the targeting we discuss the feasibility of an atlas-based automatic approach. Delineated originally on a high resolution 3D histology-MR atlas set, the NBM and the striatum could be located on the MR image of a rhesus subject through affine and nonrigid registrations. The atlas-based targeting of NBM was compared with the targeting conducted manually by an experienced neuroscientist. Based on the targeting, the trajectories and entry points for delivering the genetic probes to the targets could be established on the CT images of the subject after rigid registration. The accuracy of the targeting was assessed quantitatively by comparison between NBM locations obtained automatically and manually, and finally demonstrated qualitatively via post mortem analysis of slices that had been labelled via Evan Blue infusion and immunohistochemistry.