Hypoplastic left heart syndrome is a severe congenital heart defect requiring surgical intervention shortly after birth. The surgery is complex and often leads to complications requiring additional surgeries. Understanding the relationship between the structure of the tricuspid valve and functional complications could lead to more powerful diagnostic and treatment options. Because the tricuspid valve does not have spherical topology, many traditional methods for creating boundary-based or skeleton-based shape models that require spherical parameterization of an object are not applicable unless individual leaflets are independently parameterized and then merged in a multi-object model. Instead we propose to create skeletal models (s-reps) of the entire tricuspid valve structure using a cylindrical parameterization. We modify a traditional cylindrical parameterization approach by adaptively changing angle sampling based on landmarks to produce anatomically relevant correspondence across a population of objects. From this we derive s-reps which yield an improved shape space and classification performance compared with previous approaches.
We demonstrate the application of the Thin Shell Demons (TSD) surface registration algorithm in registering the dental scans obtained from intra-oral scanners (IOS) and Cone Beam Computed Tomography (CBCT) in a semi-automatic manner. The reconstructed dentition obtained from CBCT lacks the accuracy for diagnosis and appliance fabrication that IOS provides. Current methods to register IOS to CBCT typically use Iterative Closest Point (ICP) but suffer from a lack of precision and accuracy. TSD registration has previously been shown to produce superior registration results in presence of missing patches and holes. In this work, for the first time we share its application in dental scan registration. We perform experiments on dental surface meshes obtained from CBCT and IOS for two patients who have undergone orthognathic surgery. Our method first registers the IOS mesh with the CBCT mesh using ICP. To obtain tight alignment of the tooth surface, we perform a refinement using the TSD registration. We quantify the improvement in registration by measuring the distance between the closest points present on the surface of six teeth in the IOS and CBCT meshes. Compared to using only ICP registration, TSD decreases the mean surface distance between patches and significantly improves the alignment. We also share qualitative results that clearly demonstrate the improvement due to TSD. For wide adoption and ease of access, we also share a publicly available Thin Shell Demons implementation in C++ under the open source image processing library Insight Toolkit (ITK). Python wrapping of the code is also made available. The code is available at the url: https://github.com/InsightSoftwareConsortium/ITKThinShellDemons.
Fracture fixation surgeries require a careful and well thought out surgical plan, mainly due to the wide range of possibilities in the fracture types and available choices in fixation constructs. There is considerable interest in virtual 3D planning tools ranging from 3D visualization, interactive fracture reduction and bio-mechanical analysis of fracture fixation construct stability to arrive at optimal plan. One of the key steps prior to reconstructing 3D fractures is accurate fracture segmentation which can be tedious and time consuming even with semi-automated tools. In this paper, we report preliminary results from our attempt to fully automate the segmentation of fractured bone using deep learning. We performed experiments using the widely used 3D segmentation model called 3D U-Net on a dataset of 14 CT volumes. The dataset is randomly divided into train, validation and test splits comprising 7, 3 and 4 volumes respectively. Even with a small training set of femur fractures, we were able to achieve a mean dice score of 0.861 with a mean sensitivity of 0.899. The model was able to capture the challenging fracture regions and could cleanly separate the femur head and socket. Apart from this, we also studied the impact of different loss functions on the network’s performance. The results indicate that deep learning based segmentation methodologies have good potential in automating the challenging task of fractured femur segmentation. Further improvement is expected with a larger collection of such fractured samples
Shape analysis is an important and powerful tool in a wide variety of medical applications. Many shape analysis techniques require shape representations which are in correspondence. Unfortunately, popular techniques for generating shape representations do not handle objects with complex geometry or topology well, and those that do are not typically readily available for non-expert users. We describe a method for generating correspondences across a population of objects using a given template. We also describe its implementation and distribution via SlicerSALT, an open-source platform for making powerful shape analysis techniques more widely available and usable. Finally, we show results of this implementation on mouse femur data.
Microfractures (cracks) are the third most common cause of tooth loss in industrialized countries. If they are not detected early, they continue to progress until the tooth is lost. Cone beam computed tomography (CBCT) has been used to detect microfractures, but has had very limited success. We propose an algorithm to detect cracked teeth that pairs high resolution (hr) CBCT scans with advanced image analysis and machine learning. First, microfractures were simulated in extracted human teeth (n=22). hr-CBCT and microCT scans of the fractured and control teeth (n=14) were obtained. Wavelet pyramid construction was used to generate a phase image of the Fourier transformed scan which were fed to a U-Net deep learning architecture that localizes the orientation and extent of the crack which yields slice-wise probability maps that indicate the presence of microfractures. We then examine the ratio of high-probability voxels to total tooth volume to determine the likelihood of cracks per tooth. In microCT and hr-CBCT scans, fractured teeth have higher numbers of such voxels compared to control teeth. The proposed analytical framework provides a novel way to quantify the structural breakdown of teeth, that was not possible before. Future work will expand our machine learning framework to 3D volumes, improve our feature extraction in hr-CBCT and clinically validate this model. Early detection of microfractures will lead to more appropriate treatment and longer tooth retention.
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