Improving cervical neoplasia diagnosis via novel in vivo imaging technologies and deep learning algorithms

Two directions are explored for improving the current cervical cancer diagnosis procedure. The first investigates the future deployment of in vivo confocal imaging in the clinic, for detecting precancerous tissues, and the second proposes an algorithm for automatic interpretation of histology images (acquired by light microscopy). We acquired i) confocal microscopy images of cervical biopsies taken from 50 patients, at different tissue depths and ii) histology images of different sections cut from each biopsy. From the confocal images, we identified four features that carry enough information relevant to cell morphology and tissue architecture. We demonstrated that the relevant information in these features is comparable to that extracted from the same features in histology images. This implies that we can obtain the relevant information from confocal imaging, without having to cut a biopsy from the patient’s cervix. We then studied the confocal images and determined the grade lesion of every biopsy and found that confocal imaging resulted in less false positives than the diagnosis given by the gynecologist (based on the appearance of the cervix under colposcopy). Utilizing confocal microscopy technology in the clinic would thus decrease the number of unnecessary biopsies. We then developed a deep learning algorithm that automatically and quantitatively assesses HPV contaminated and proliferating cells in histology images of biopsy sections. The automatic assessment of this procedure is important as it plays a significant role in differentiating between disease grades but forms a challenging and complex task and demands a large amount of time when performed manually by a pathologist. We demonstrated that this algorithm could help the pathologists to differentiate between different grades of cervical precancerous tissues. Our results are also more reproducible compared to other methods (like color deconvolution) that are widely being used in the field of digital pathology. The in vivo imaging and automatic image analysis algorithms demonstrated in the thesis can potentially enable i) real time diagnosis in the clinic, and ii) fast interpretation of histology images in a reproducible and cost-effective manner. While developed for cervical neoplasia, these methods could be extended to oral cavity, skin, and other epithelial tissue cancers.Applied Science, Faculty ofBiomedical Engineering, School ofGraduat

Spatial Dispersion of Lesions as a Surrogate Biomarker for Disability in Multiple Sclerosis

Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability. In this study, we examine the ability of four different measures of lesion dispersion including one connectedness-based measure (compactness), one regionbased measure (ratio of lesion convex hull to brain volume) and two distance-based measures (Euclidean distance from a fixed point and pair-wise Euclidean distances) to act as potential surrogate markers of disability. We use a set of T2-weighted and proton density-weighted MRIs of 24 MS patients, collected from a single selected scanning site participating in an MS clinical trial. For each patient, clinica

Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks - Fig 5

<p>Heat-maps resulting from biomarker labeling on an example IHC image (a) using WI-Net. The IHC image (size 2048×2048 pixels) belonging to a whole slide image obtained from the scanner was fed to the WI-Net (a). The outputs of the WI-Net were the heat-maps (size 2048 × 2048 pixels) marking p16 positive regions (b), Ki-67 positive regions (c), p16 and Ki-67 positive regions (d), p16 and Ki-67 negative regions (e). The four heat-maps were combined to produce an overall biomarker heat-map (f).</p

Sample of nuclei images obtained from an IHC image.

<p>ROI (red enclosure) selected in an IHC image, within which the nuclei were segmented in order to obtain nuclei images for training N-Net (left). Nuclei images expressing different proteins (right); p16 positive, Ki-67 positive, p16 and Ki-67 positive, and p16 and Ki-67 negative.</p

Comparison between color deconvolution approach and WI-Net approach for locating p16 and Ki-67 positive pixels in two IHC images.

<p>The IHC image on the left two columns corresponds to a high-grade cervical lesion. The IHC image on the right two columns corresponds to a normal cervical epithelium. The first row shows the RGB images. The second row shows the regions marked as p16 positive by the two methods. The third row shows the regions marked as Ki-67 positive by the two methods.</p

Schematic representation of the architecture of the WI-Net.

<p>The first two layers of WI-Net (convolution and max-pooling layers) are the same as the first two layers of N-Net illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190783#pone.0190783.g002" target="_blank">Fig 2</a>. The last three layers of WI-Net are convolution layers. The input to the WI-Net is a whole IHC image, and the output is a heat-map of the present biomarker(s) in the IHC image.</p

Distribution of nuclei in the training and test sets.

<p>Distribution of nuclei in the training and test sets.</p

Schematic representation of the architecture of the N-Net.

<p>N-Net consists of one convolution layer, followed by a max-pooling layer and two fully connected layers. The last layer is the output layer. This network takes an RGB image of a nucleus as input and generates a label as the output.</p