Development and Validation of a Three-Dimensional Optical Imaging System for Chest Wall Deformity Measurement

Congenital chest wall deformities (CWD) are malformations of the thoracic cage that become more pronounced during early adolescence. Pectus excavatum (PE) is the most common CWD, characterized by an inward depression of the sternum and adjacent costal cartilage. A cross-sectional computed tomography (CT) image is mainly used to calculate the chest thoracic indices. Physicians use the indices to quantify PE deformity, prescribe surgical or non-surgical therapies, and evaluate treatment outcomes. However, the use of CT is increasingly causing physicians to be concerned about the radiation doses administered to young patients. Furthermore, radiographic indices are an unsafe and expensive method of evaluating non-surgical treatments involving gradual chest wall changes. Flexible tape or a dowel-shaped ruler can be used to measure changes on the anterior side of the thorax; however, these methods are subjective, prone to human error, and cannot accurately measure small changes. This study aims to fill this gap by exploring three-dimensional optical imaging techniques to capture patients’ chest surfaces. The dissertation describes the development and validation of a cost-effective and safe method for objectively evaluating treatment progress in children with chest deformities. First, a study was conducted to evaluate the performance of low-cost 3D scanning technologies in measuring the severity of CWD. Second, a multitemporal surface mesh registration pipeline was developed for aligning 3D torso scans taken at different clinical appointments. Surface deviations were assessed between closely aligned scans. Optical indices were calculated without exposing patients to ionizing radiation, and changes in chest shape were visualized on a color-coded heat map. Additionally, a statistical model of chest shape built from healthy subjects was proposed to assess progress toward normal chest and aesthetic outcomes. The system was validated with 3D and CT datasets from a multi-institutional cohort. The findings indicate that optical scans can detect differences on a millimeter scale, and optical indices can be applied to approximate radiographic indices. In addition to improving patient awareness, visual representations of changes during nonsurgical treatment can enhance patient compliance

Spatial Audio and Individualized HRTFs using a Convolutional Neural Network (CNN)

Spatial audio and 3-Dimensional sound rendering techniques play a pivotal and essential role in immersive audio experiences. Head-Related Transfer Functions (HRTFs) are acoustic filters which represent how sound interacts with an individual's unique head and ears anatomy. The use of HRTFs compliant to the subjects anatomical traits is crucial to ensure a personalized and unique spatial experience. This work proposes the implementation of an HRTF individualization method based on anthropometric features automatically extracted from ear images using a Convolutional Neural Network (CNN). Firstly, a CNN is implemented and tested to assess the performance of machine learning on positioning landmarks on ear images. The I-BUG dataset, containing ear images with corresponding 55 landmarks, was used to train and test the neural network. Subsequently, 12 relevant landmarks were selected to correspond to 7 specific anthropometric measurements established by the HUTUBS database. These landmarks serve as a reference for distance computation in pixels in order to retrieve the anthropometric measurements from the ear images. Once the 7 distances in pixels are extracted from the ear image, they are converted in centimetres using conversion factors, a best match method vector is implemented computing the Euclidean distance for each set in a database of 116 ears with their corresponding 7 anthropometric measurements provided by the HUTUBS database. The closest match of anthropometry can be identified and the corresponding set of HRTFs can be obtained for personnalized use. The method is evaluated in its validity instead of the accuracy of the results. The conceptual scope of each stage has been verified and substantiated to function correctly. The various steps and the available elements in the process are reviewed and challenged to define a greater algorithm entity designed for the desired task

Quick, accurate, smart: 3D computer vision technology helps assessing confined animals' behaviour

<p>(a) Visual representation of the alignment of two sequences using the Dynamic Time Warping (DTW). The DTW stretches the sequences in time by matching the same point with several points of the compared time series. (b) The Needleman Wunsh (NW) algorithm substitutes the temporal stretch with gap elements (red circles in the table) inserting blank spaces instead of forcefully matching point. The alignment is achieved by arranging the two sequences in this table, the first sequence row-wise (T) and the second column-wise (S). The figure shows a score table for two hypothetical sub-sequences (i, j) and the alignment scores (numbers in cells) for each pair of elements forming the sequence (letters in head row and head column). Arrows show the warping path between the two series and consequently the final alignment. The optimal alignment score is in the bottom-right cell of the table.</p

How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network

Computational modeling of artwork meaning is complex and difficult. This is because art interpretation is multidimensional and highly subjective. This paper experimentally investigated the degree to which a state-of-the-art Deep Convolutional Neural Network (DCNN), a popular Machine Learning approach, can correctly distinguish modern conceptual art work into the galleries devised by art curators. Two hypotheses were proposed to state that the DCNN model uses Exhibited Properties for classification, like shape and color, but not Non-Exhibited Properties, such as historical context and artist intention. The two hypotheses were experimentally validated using a methodology designed for this purpose. VGG-11 DCNN pre-trained on ImageNet dataset and discriminatively fine-tuned was trained on handcrafted datasets designed from real-world conceptual photography galleries. Experimental results supported the two hypotheses showing that the DCNN model ignores Non-Exhibited Properties and uses only Exhibited Properties for artwork classification. This work points to current DCNN limitations, which should be addressed by future DNN models

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Automated body volume acquisitions from 3D structured-light scanning

Whole-body volumes and segmental volumes are highly related to the health and medical condition of individuals. However, the traditional manual post-processing of raw 3D scanned data is time-consuming and needs technical expertise. The purpose of this study was to develop bespoke software for obtaining whole-body volumes and segmental volumes from raw 3D scanned data automatically and to establish its accuracy and reliability. The bespoke software applied Stitched Puppet model fitting techniques to deform template models to fit the 3D raw scanned data to identify the segmental endpoints and determine their locations. Finally, the bespoke software used the location information of segmental endpoints to set segmental boundaries on the reconstructed meshes and to calculate body volume. The whole-body volumes and segmental volumes (head & neck, torso, arms, and legs) of 29 participants processed by the traditional manual operation were regarded as the references and compared to the measurements obtained with the bespoke software using the intra-method and inter-method relative technical errors of measurement. The results showed that the errors in whole-body volumes and most segmental volumes acquired from the bespoke software were less than 5%. Overall, the bespoke software developed in this study can complete the post-processing tasks without any technical expertise, and the obtained whole-body volumes and segmental volumes can achieve good accuracy for some applications in health and medicine