Diffuse Reflectance Spectroscopy (DRS) is a leading technique for the detection of head and neck cancers. It can capture information regarding tissue absorption and scattering. In this research work, we propose a novel method for the identification of normal and Squamous Cell Carcinoma (SCC) mucosa tissues using the Bag Of Words (BOW) approach. The study included 70 spectra from normal mucosa tissue sites and 70 spectra from SCC mucosa tissue sites. First, the spectra are preprocessed by extracting the useful wavelength range, denoising and reducing the inter and intra patient variability. Subsequently, features are extracted from each spectrum by continuously sliding a window with a predefined length along each spectrum to extract a group of local segments. Discrete Wavelet transform (DWT) is then employed for each segment. Next, we construct the codebook to represent each spectrum by a histogram of codewords at which each bin in the histogram is a count of a codeword appeared in the spectrum. Finally, the histogram representation is used as input for classification. The maximum accuracy reported is 94.28% with sensitivity and specificity of 91.42% and 97.14% respectively.

The Bag of Words Approach for the Classification of Head and Neck Cancers Using Diffuse Reflectance Spectroscopy

Transitive consistency of pairwise transformations is a desirable property of groupwise image registration procedures. The transformation synchronisation method (Bernard et al., 2015) is able to retrieve transitively consistent pairwise transformations from pairwise transformations that are initially not transitively consistent. In the present paper, we present a numerically stable implementation of the transformation synchronisation method for affine transformations, which can deal with very large translations, such as those occurring in medical images where the coordinate origins may be far away from each other. By using this method in conjunction with any pairwise (affine) image registration algorithm, a transitively consistent and unbiased groupwise image registration can be achieved.
Experiments involving the average template generation from 3D brain images demonstrate that the method is more robust with respect to outliers and achieves higher registration accuracy compared to reference-based registration.

Transitively Consistent and Unbiased Multi-Image Registration using Numerically Stable Transformation Synchronisation

Very preterm birth coincides with a period of major development in the brain, with striking changes in volume, cortex folding and significant change at the microstructural level. Diffusion MRI is sensitive to motion of water on the scale of microns, allowing us to investigate some of these changes. Mapping of diffusion tensors is a challenging process, and existing methods fail to account for the major changes that take place between 30 and 40 weeks equivalent gestational age. In this paper we introduce the spectral matching in the context of non-linear registration of diffusion images. Spatial correspondences are defined with respect to the main spectral modes of the images, which are global descriptors of the tensor information. We apply tensor spectral matching (TSM) in two different ways: by estimation of spatial correspondences uniquely based on the spectral decomposition of diffusion tensor images, and by combination of TSM with a standard diffusion tensor registration algorithm (TSM-DTI-TK). We validate the proposed approaches on 20 adult controls, and we compare it to the state-of-art registration method. We then apply these methods to longitudinal diffusion data acquired from 6 extremely preterm-born infants scanned shortly after birth and at term equivalent age. The experimental results combining TSM with standard tensor registration outperform the state-of-art when applied to both adult and preterm data. Having a reliable anatomical correspondence in preterm infants allows us to quantify microstructural changes and to work towards developing biomarkers of neurological impairment.

Tensor Spectral Matching of Diffusion Weighted Images

The Gyrification Index (GI) quantifies the amount of folding of a cortical surface. In this paper, we show the efficiency of spectral analysis to perform such a task, and in particular we explain how the graph windowed Fourier transform can be used as a tool to define new GIs with multiscale properties. We propose two different GIs and study their effect on a set of subjects whose cortical surfaces are modeled with triangular meshes.

The graph windowed Fourier transform: a tool to quantify the gyrification of the cerebral cortex

Medical image analyses rely on diverse software packages assembled into a “pipeline”. The Java Image Science Toolkit (JIST) has served as a standalone plugin into the Medical Image Processing Analysis and Visualization (MIPAV). We addressed shortcomings that previously prevented deeper integration of JIST with other E-science platforms. First, we developed an interface for integrating externally compiled packages (similar to the interfaces in NiPy) such that the application can become a “draggable module” in the module tree. This allows for connection of inputs and outputs to other JIST modules while maintaining external processing and monitoring. Second, we develop an integration interface with the Neuroimaging Informatics Tools and Resources Clearinghouse Cloud Environment (NITRC-CE). User can launch and terminate pre-configured nodes to utilize computational resources of the Amazon cloud. Finally, we define a new external data source, which can connect to the eXtensible Neuroimaging Archive Toolkit (XNAT) to query and retrieve remote data using XNAT's REST API. Specifically, we define dataflow for files that can readily be converted into volumes and collections of volumes to interface with any JIST module that expects volumetric image data as an input. Users now have the ability to run their pipelines from a well-defined external data source and no longer are required to already have data on the disk. With these upgrades we have extended JIST’s capabilities outside of complied java source code and enhanced capabilities to seamlessly interface with E-science platforms. 

Integration of the Java Image Science Toolkit with E-Science Platform

A multi-atlas approach is proposed for the automatic segmentation of nine different structures in a set of head and neck CT images for radiotherapy. The approach takes advantage of a training dataset of 25 images to build average head and neck atlases of high-quality. By registering patient images with the atlases at the global level, structures of interest are aligned approximately in space, which allowed multi-atlas-based segmentations and correlation-based label fusion to be performed at the local level in the following steps. Qualitative and quantitative evaluations are performed on a set of 15 testing images. As shown by the results, mandible, brainstem and parotid glands are segmented accurately (mean volume DSC>0.8). The segmentation accuracy for the optic nerves is also improved over previously reported results (mean DSC above 0.61 compared with 0.52 for previous results). 

A Multi-atlas Approach for the Automatic Segmentation of Multiple Structures in Head and Neck CT Images

A new patch based label fusion method based on generative approach is proposed for segmentation of mandible, brainstem, parotid and submandibular glands, optic nerves and the optic chiasm in head and neck CT images. The proposal constructs local classifiers from a dictionary of patches and weights their contribution using a generative probabilistic criterion. Also, a gaussian slide window is used to weight the multiples estimations of neighboring voxels. The proposed method was evaluated on a set of 15 CT images (10 off-site and 5 onsite) provided by the organizers of the Head and neck Auto-Segmentation challenge(MICCAI 2015), where the obtained results are comparable to many of the other methods used in the challenge. 

Head and Neck Auto Segmentation Challenge based on Non-Local Generative Models

We present a fully automatic model based system for segmenting the mandible, parotid and submandibular glands, brainstem, optic nerves and the optic chiasm in CT images, which won the MICCAI 2015 Head and Neck Auto Segmentation Grand Challenge. The method is based on Active Appearance Models (AAM) built from manually segmented examples via a cancer imaging archive provided by the challenge organisers. High quality anatomical correspondences for the models are generated using a Minimum Description Length (MDL) Groupwise Image Registration method. A multi start optimisation scheme is used to robustly match the model to new images. The model has been cross validated on the training data to a good degree of accuracy, and successfully segmented all the test data. 


Fully Automatic Segmentation of Head and Neck Organs using Active Appearance Models

We describe a segmentation method that was used in the Head and Neck Auto Segmentation Challenge held at the MICCAI 2015 conference. The algorithm consists of two building blocks. First, we employ a multi-atlas segmentation to obtain an initial segmentation for the considered organs at risk. Secondly, we use an Active Shape Model (ASM) segmentation to refine the initial segmentation of some of the organs. Leave-one-out experiments with the training data were used to determine suitable parameters for the individual steps of the segmentation. The ASM refinement resulted in improved segmentation for the optic nerves and submandibular glands, while for the brain stem, parotid glands, chiasm, and mandibular bone, the multi-atlas segmentation was preferable. Our submission achieved the second rank in the challenge.

Multi Atlas Segmentation with Active Shape Model Refinement for Multi-Organ Segmentation in Head and Neck Cancer Radiotherapy Planning

The Insight Segmentation and Registration Toolkit (www.itk.org) has become a standard in academia and industry for medical image analysis. In recent years, the ITK developers' community has focused on providing programming interfaces to ITK from Python, Java, and Javascript and making ITK available via leading applications such as Slicer and ImageJ. In this course we present best practices for taking advantage of ITK in your imaging research and commercial products. We demonstrate how script writing and interactive GUIs can be used to access the algorithms in ITK and the multitude of ITK extensions that are freely available on the web. We also cover the opportunities and challenges with using open-source software in research and in commercial applications: from prototypes that can lead to venture capital funding to applications for first-in-human trials and ultimately for regulatory approval.