Deep Structured Models for Large Scale Object Co-detection and Segmentation

Structured decisions are often required for a large variety of image and scene understanding tasks in computer vision, with few of them being object detection, localization, semantic segmentation and many more. Structured prediction deals with learning inherent structure by incorporating contextual information from several images and multiple tasks. However, it is very challenging when dealing with large scale image datasets where performance is limited by high computational costs and expressive power of the underlying representation learning techniques. In this thesis, we present efficient and effective deep structured models for context-aware object detection, co-localization and instance-level semantic segmentation. First, we introduce a principled formulation for object co-detection using a fully-connected conditional random field (CRF). We build an explicit graph whose vertices represent object candidates (instead of pixel values) and edges encode the object similarity via simple, yet effective pairwise potentials. More specifically, we design a weighted mixture of Gaussian kernels for class-specific object similarity, and formulate kernel weights estimation as a least-squares regression problem. Its solution can therefore be obtained in closed-form. Furthermore, in contrast with traditional co-detection approaches, it has been shown that inference in such fully-connected CRFs can be performed efficiently using an approximate mean-field method with high-dimensional Gaussian filtering. This lets us effectively leverage information in multiple images. Next, we extend our class-specific co-detection framework to multiple object categories. We model object candidates with rich, high-dimensional features learned using a deep convolutional neural network. In particular, our max-margin and directloss structural boosting algorithms enable us to learn the most suitable features that best encode pairwise similarity relationships within our CRF framework. Furthermore, it guarantees that the time and space complexity is O(n t) where n is the total number of candidate boxes in the pool and t the number of mean-field iterations. Moreover, our experiments evidence the importance of learning rich similarity measures to account for the contextual relations across object classes and instances. However, all these methods are based on precomputed object candidates (or proposals), thus localization performance is limited by the quality of bounding-boxes. To address this, we present an efficient object proposal co-generation technique that leverages the collective power of multiple images. In particular, we design a deep neural network layer that takes unary and pairwise features as input, builds a fully-connected CRF and produces mean-field marginals as output. It also lets us backpropagate the gradient through entire network by unrolling the iterations of CRF inference. Furthermore, this layer simplifies the end-to-end learning, thus effectively benefiting from multiple candidates to co-generate high-quality object proposals. Finally, we develop a multi-task strategy to jointly learn object detection, localization and instance-level semantic segmentation in a single network. In particular, we introduce a novel representation based on the distance transform of the object masks. To this end, we design a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. We show that the predicted masks can go beyond the scope of the bounding boxes and that the multiple tasks can benefit from each other. In summary, in this thesis, we exploit the joint power of multiple images as well as multiple tasks to improve generalization performance of structured learning. Our novel deep structured models, similarity learning techniques and residual-deconvolution architecture can be used to make accurate and reliable inference for key vision tasks. Furthermore, our quantitative and qualitative experiments on large scale challenging image datasets demonstrate the superiority of the proposed approaches over the state-of-the-art methods

Person re-identification via efficient inference in fully connected CRF

In this paper, we address the problem of person re-identification problem, i.e., retrieving instances from gallery which are generated by the same person as the given probe image. This is very challenging because the person's appearance usually undergoes significant variations due to changes in illumination, camera angle and view, background clutter, and occlusion over the camera network. In this paper, we assume that the matched gallery images should not only be similar to the probe, but also be similar to each other, under suitable metric. We express this assumption with a fully connected CRF model in which each node corresponds to a gallery and every pair of nodes are connected by an edge. A label variable is associated with each node to indicate whether the corresponding image is from target person. We define unary potential for each node using existing feature calculation and matching techniques, which reflect the similarity between probe and gallery image, and define pairwise potential for each edge in terms of a weighed combination of Gaussian kernels, which encode appearance similarity between pair of gallery images. The specific form of pairwise potential allows us to exploit an efficient inference algorithm to calculate the marginal distribution of each label variable for this dense connected CRF. We show the superiority of our method by applying it to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure

Development of a completely biological tissue engineered heart valve.

University of Minnesota Ph.D. dissertation. April 2009. Major: Chemical Engineering. Advisor: Robert T. Tranquillo. 1 computer file (PDF); xii, 215 pages, appendices A-C.In the United States alone, over 100,000 heart valve replacement procedures are performed each year, with approximately 45% of patients below age 65. While current mechanical and bioprosthetic heart valves are viable options, they have several limitations. The most significant limitation is for pediatric patients, since neither of these valve types grow and remodel with the patient. Tissue engineering provides a methodology to create functional heart valves that can grow and remodel similar to native tissue once implanted. Several tissue engineering approaches have been proposed using decellularized native scaffolds, synthetic biopolymers, and biological polymers seeded with cells. Fibrin provides a scaffold to create tissue-engineered heart valves (TEHV) that are completely biological with an environment permissive for extracellular matrix (ECM) deposition. Previous research in our lab has demonstrated the feasibility of creating a fibrin-based TEHV with neonatal human dermal fibroblast (nHDF) that yields valve leaflets with structural and mechanical anisotropy similar to native leaflets. However, the TEHV had sub-optimal tensile mechanical properties and was thus unable to withstand physiological forces. The development of tissue can be accelerated by both chemical and mechanical stimulus. Previously, fibrin based TEHV were cultured with chemical stimulus in the form of growth factors supplemented in the culture medium resulting in improved ECM deposition by the cells; however, no mechanical stimulation was applied. Prior research in both our lab and by other researchers has shown cyclic stretching with constant strain amplitude is a method to stimulate remodeling of biological scaffolds seeded with cells. Initial experiments were conducted to evaluate the effect of cyclic stretching on fibrin-based tubular constructs seeded with porcine valve interstitial cells (PVIC) and nHDF. Cyclic stretching with 10% constant strain amplitude applied for 3 weeks led to modest improvement in tensile properties of the tubular constructs. We hypothesized that long-term cyclic stretching, as was used in this study, could induce cellular adaptation, minimizing the benefits of cyclic stretching. This hypothesis was tested in subsequent experiments using tubular constructs cultured with incremental strain amplitude cyclic stretching, with an average strain of 10% for 3 weeks. Both PVIC and nHDF seeded constructs exhibited a 2-fold improvement in ultimate tensile strength (UTS) and collagen density over samples conditioned with constant strain amplitude strteching. To verify that this was the result of a cellular response, phosphorylation of extracellular signal-regulated kinase (ERK) was measured by western blot. At 5 weeks, the phosphorylated ERK was 255% higher in incremental cyclic strained samples compared to constant strain samples. nHDF-seeded tubular constructs were also used to optimize the use of transforming growth factor beta (TGF-β). Studies showed that under cyclic stretching conditions, TGF-β has detrimental effects on total collagen deposition and collagen maturation. Western blot analysis showed a decrease in p-ERK signaling in TGF-β treated samples. However, TGF-β use demonstrated a benefit by increasing the elastin content of the tissue constructs. In subsequent experiments, a sequence of cyclic stretching and TGF-β supplementation was used to optimize tensile mechanical properties and elastin content of the engineered tissue. Based on the results with tubular constructs, a novel bioreactor was designed to apply controlled cyclic stretching to the fibrin-based TEHV. Briefly, the valve was mounted on two plastic end-pieces with elastic latex tube placed around TEHV. Using a reciprocating syringe pump, culture medium was cyclically pumped into the bioreactor. The root distension, which was determined by the stiffer latex was used as a control parameter, and in turn stretched the leaflets. A separate flowloop (connected to the bioreactor end-pieces) was used to control nutrient transport to the TEHV. Using an incremental strain amplitude stretching regime, fibrin-based TEHV were conditioned in the bioreactor for 3 weeks. Cyclically stretched valves (CS valve) had improved tensile properties and collagen deposition compared to statically-cultured valves. The mechanical stiffness (modulus) and anisotropy (measured as ratio of leaflet modulus in circumferential to radial directions) in the leaflets was comparable to native sheep pulmonary valve leaflets. Collagen organization/ maturation also improved in CS valves over statically-cultured valves as observed by picrosirius red staining of tissue crosssections. In addition, the CS valve root could withstand pressures of up to 150 mmHg and its compliance was comparable to that of the sheep pulmonary artery at physiological pressures. To assess in vivo remodeling TEHV were implanted in the pulmonary artery of two sheep for 4.5 weeks with the pulmonary valve either left intact or rendered incompetent by leaflet excision. Echocardiography immediately after implantation showed functional coapting leaflets, with normal right heart function. It was also performed just prior to explantation, revealing functional leaflets although with moderate regurgitation in both cases and a partial detachment of one leaflet from the root in one case. The explanted leaflets had thickness and tensile properties comparable the implanted leaflets. There was endothelialization on the lumenal surface of the TEHV root. These preliminary results are unprecedented for a TEHV developed from a biological scaffold; however, many issues remain to be surmounted. In further development of the TEHV with a fibrin scaffold, photo-cross linking of the fibrin gel was utilized as a method to stiffen the matrix, thereby inhibiting excessive cell-induced compaction. Preliminary studies with tubular constructs demonstrated reduced compaction of cross-linked fibrin gel during cyclic stretching with no effect on nHDF proliferation or deposited collagen. In addition, a preliminary investigation using blood outgrowth endothelial cells (BOEC) has been conducted to assess their adhesion to the remodeled TEHV surface. Studies showed BOEC adhesion and proliferation on remodeled fibrin surface creating a confluent layer after 4 days of culture. Successful seeding of sheep BOEC on the TEHV surface prior to implantation would reduce the risk of clotting. Overall, the studies presented in this dissertation advance the development of a completely biological tissue-engineered heart valve. These studies improve our understanding of the role of cyclic stretching in tissue remodeling and have furthered the science of mechnotransduction and tissue remodeling

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population