Detection of Features to Track Objects and Segmentation Using GrabCut for Application in Marker-less Augmented Reality

AbstractAugmented Reality applications have hovered itself over various platforms such as desktop and most recently to handheld devices such as mobile phones and tablets. Augmented Reality (AR) systems have mostly been limited to Head Worn Displays with start-ups such as Magic Leap and Occulus Rift making tremendous advancement in such AR and VR research applications facing a stiff competition with Software giant Microsoft which has recently introduced Holo Lens. AR refers to the augmentation or the conglomeration of virtual objects in the real world scenario which has a distinct but close resemblance to Virtual Reality (VR) systems which are computer simulated environments which render physical presence in imaginary world. Developers and hackers round the globe have directed their research interests in the development of AR and VR based applications especially in the domain of advertisement and gaming. Many open source libraries, SDKs and proprietary software are available worldwide for developers to make such systems. This paper describes an algorithm for an AR prototype which uses a marker less approach to track and segment out real world objects and then overlay the same on another real world scene. The algorithm was tested on Desktop. The results are comparable with other existing algorithms and outperform some of them in terms of robustness, speed, and accuracy, precision and timing analysis

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

Spine segmentation in computed tomography images using geometric flows and shape priors

Surgical treatment of spine-related injuries requires the placement of pedicle screws. Precise localization of the individual vertebrae and surrounding tissues is thus essential toavoid damage to nearby regions. Image-guided surgery can help in surgical planning andthus improve prognosis. During surgery, preoperative patient scans are registered to intraoperative scans which allows surgeons to track the location of the surgical toolsand better visualize their position with respect to the actual anatomy. In this thesis, wepresent a semi-automated pipeline to segment the human spinal column in computed tomography scans. These segmented anatomical structures wouldthus act as a model to which the intraoperative scans are later registered. We incorporate ashape prior into geometric active contours to augment the segmentation produced using region andboundary based terms. We have also applied ideas based on anisotropic diffusion and fluxcomputation to preprocess the volumes to address the challenges faced when working with CT scans, such as region inhomogeneities within and outside the spine and a lack of signalat the vertebral boundaries due to partial volume effects. We validateour approach on three datasets and achieve results comparable to the state of the art. Our method alsoprovides good localization and segmentation of the spinal canal and intervertebral discs as offshoots.Les opérations chirurgicales des lésions de la colonne vertébrale nécessitent la mise en place de vis pédiculaires. Localiser précisément les vertèbres et les tissus environnants est donc indispensable pour éviter dendommager les régions voisines. Guider le chirurgien par l’image aide à la planification chirurgicale et améliore donc le pronostic. Pendantl’opération, des scans préopératoires des patients sont recalés sur des images captées en direct, ce qui permet aux chirurgiens de localiser leurs outils et mieux visualiser leur position par rapport à l’anatomie du patient. Dans cette thése, nous présentons un processus semi-automatisé pour segmenter des scanners de tomodensitométrie et en extraire la colonne vertébrale. Ainsi segmentées, ces structures peuvent alors servir de modéle pour un recalage futur au moment de l’opération. Nous utilisons des contours géométriques actifs, s’appuyant sur la détection des contours, des régions, et utilisant une connaissance a priori de ces formes afin d’affiner la segmentation. Nous avons également utilisé des principes de la diffusion anisotrope et de calculs de flux afin de prétraiter les volumes. En effet, il s’agit de répondre aux défis usuels de la tomodensitométrie, comme par exemple l’hétérogénéité à l’intérieur et à l'extérieur de la colonne vertébrale ou les perturbations liées à la limite de résolution. Nous validons notre approche sur trois jeux de données et nous obtenons des résultats comparables à l’état de l’art. De notre méthode découle aussi une bonne localisation et une bonne segmentation du canal rachidien et des disques intervertébraux

Impacted foreign bodies in the maxillofacial region–A series of three cases

Penetrating injuries to the maxillofacial region are very common. Foreign bodies embedded deep in the maxillofacial region due to these injuries pose a challenge to an oral and maxillofacial surgeon. These objects may become a potent source of pain and infection. Early diagnosis of these foreign bodies can be achieved by the use of plain radiographs, ultrasonography, computed tomographic scans, and magnetic resonance imaging. Once diagnosed and located, these foreign bodies should be removed. Here, we report three such cases where early diagnosis of these foreign bodies embedded in the maxillofacial region lead to their early and successful removal without complications

Performance evaluation for automated segmentation of Hippocampus Subfields: Preliminary Results using FreeSurfer and ASHS

The hippocampus has an important role in memory function and has been shown to be affected in neurodegenerative diseases at an early stage. Therefore, the hippocampus and its subfields has been an active area of research. The automated segmentation of hippocampal subfields has been shown to be feasible and there are currently two widely used tools, ASHS [Automatic Segmentation of Hippocampal Subfields](1) and FreeSurfer(2). We investigated the reliability and reproducibility of both pipelines in a high resolution 7T MRI dataset

Prevalence, characteristics, and morphology of supernumerary teeth among patients visiting a dental institution in Rajasthan

Context: Supernumerary teeth/tooth (ST) is a developmental anomaly of dentition. Variation in developmental and eruption pattern of ST can lead to the development of numerous complications in oral cavity. Aim: The aim of this study was to investigate prevalence, characteristics, and morphology of ST among patients visiting a dental institution in Rajasthan, India. Materials and Methods: During 1-year study, clinical examination of 9248 participants was performed. Morphology, type, location, number, position of eruption, state of eruption of ST, and associated complications were determined. Correlations between location of ST based on position of eruption, state of eruption, and associated complications were also determined using Chi-square test. Results: The frequency of presence of ST in the studied population was 0.63% (58 participants). In these 58 participants, eighty-two supernumeraries were found. Forty-six participants (79.32%) presented with one ST. Males were more affected than females (2.05:1), and the maxilla was the most commonly affected region. Among 82 identified supernumeraries, we noted highest incidence of parapremolars (39.02%) and conical morphology (46.35%). The most common position of eruption was normal (68.30%) and most of these 82 teeth were erupted in oral cavity (57.31%). These ST have led to various endodontic, orthodontic, periodontal, and other complications in the studied population. The relation of varying complications with different location of supernumerary was found to be highly significant (P = 0.000). Conclusion: ST are best detected and diagnosed by thorough clinical examination and radiographic investigation. Early detection and adequate treatment plan should eradicate the potential future complications caused by ST

Deep label fusion : A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality