Application of Deep Learning Convolutional Neural Networks for Internal Tablet Defect Detection: High Accuracy, Throughput, and Adaptability.

Tablet defects encountered during the manufacturing of oral formulations can result in quality concerns, timeline delays, and elevated financial costs. Internal tablet cracking is not typically measured in routine inspections but can lead to batch failures such as tablet fracturing. X-ray computed tomography (XRCT) has become well-established to analyze internal cracks of oral tablets. However, XRCT normally generates very large quantities of image data (thousands of 2D slices per data set) which require a trained professional to analyze. A user-guided manual analysis is laborious, time-consuming, and subjective, which may result in a poor statistical representation and inconsistent results. In this study, we have developed an analysis program that incorporates deep learning convolutional neural networks to fully automate the XRCT image analysis of oral tablets for internal crack detection. The computer program achieves robust quantification of internal tablet cracks with an average accuracy of 94%. In addition, the deep learning tool is fully automated and achieves a throughput capable of analyzing hundreds of tablets. We have also explored the adaptability of the deep learning analysis program toward different products (e.g., different types of bottles and tablets). Finally, the deep learning tool is effectively implemented into the industrial pharmaceutical workflow.

Evaluation of segmentation methods on head and neck CT: Auto-segmentation challenge 2015.

PURPOSE: Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. METHODS: In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. RESULTS: This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. CONCLUSIONS: The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.

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.

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.

Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images.

PURPOSE: Intensity-modulated radiation therapy (IMRT) is the state of the art technique for head and neck cancer treatment. It requires precise delineation of the target to be treated and structures to be spared, which is currently done manually. The process is a time-consuming task of which the delineation of lymph node regions is often the longest step. Atlas-based delineation has been proposed as an alternative, but, in the authors' experience, this approach is not accurate enough for routine clinical use. Here, the authors improve atlas-based segmentation results obtained for level II-IV lymph node regions using an active shape model (ASM) approach. METHODS: An average image volume was first created from a set of head and neck patient images with minimally enlarged nodes. The average image volume was then registered using affine, global, and local nonrigid transformations to the other volumes to establish a correspondence between surface points in the atlas and surface points in each of the other volumes. Once the correspondence was established, the ASMs were created for each node level. The models were then used to first constrain the results obtained with an atlas-based approach and then to iteratively refine the solution. RESULTS: The method was evaluated through a leave-one-out experiment. The ASM- and atlas-based segmentations were compared to manual delineations via the Dice similarity coefficient (DSC) for volume overlap and the Euclidean distance between manual and automatic 3D surfaces. The mean DSC value obtained with the ASM-based approach is 10.7% higher than with the atlas-based approach; the mean and median surface errors were decreased by 13.6% and 12.0%, respectively. CONCLUSIONS: The ASM approach is effective in reducing segmentation errors in areas of low CT contrast where purely atlas-based methods are challenged. Statistical analysis shows that the improvements brought by this approach are significant.