Automated Segmentation of Cervical Intervertebral Disks from Videofluorography Using a Convolutional Neural Network and its Performance Evaluation Dysphagia has become an important issue in many countries, and there is a strong need for elucidating the causes of dysphagia. One of the promising ways is to analyze the static and dynamic mechanisms of cervical structures, such as epiglottises, hyoid bones, and cervical vertebral bodies, based on medical images. In this study, we propose an automated segmentation method of cervical intervertebral disks (IDs) from videofluorography (VF) by use of a convolutional neural network (CNN). First, cervical masks are extracted from the frame images of VF, and then a patchbased CNN is applied to the cervical masks to obtain the probability images of ID regions. The sizes of patches are changed in a certain range, and pixel values in the patches are normalized. Morphological image filters are applied to the probability images to eliminate falsepositive pixels. The proposed method is applied to VF of 58 participants, consisting of 39 healthy people and 19 patients. The segmentation results are compared with the ground truth as determined by a medical doctor and are evaluated with the pixelwise Fmeasure. The Fmeasure is highest (0.880) when the patch size is 21 xc3x97 21 pixels and the both of the pixel value normalization (PVN) and the false positive elimination (FPE) are applied. On the other hand, the Fmeasure is lowest (0.443) when the patch size is 15 xc3x97 15 pixels and neither PVN nor FPE is applied.

Cervical Cancer Diagnosis using CervixNet  A Deep Learning Approach Cervical cancer affects 570,000 women globally and is among the most common causes of cancerrelated deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide lowcost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Biomedical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and MobileODT Kaggle dataset giving an Accuracy of 96.77% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost.