Synthesis and characterization of fused conjugated materials for organic electronics

Ph.DDOCTOR OF PHILOSOPH

PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation

Effective training of deep image segmentation models is challenging due to the need for abundant, high-quality annotations. Generating annotations is laborious and time-consuming for human experts, especially in medical image segmentation. To facilitate image annotation, we introduce Physics Informed Contour Selection (PICS) - an interpretable, physics-informed algorithm for rapid image segmentation without relying on labeled data. PICS draws inspiration from physics-informed neural networks (PINNs) and an active contour model called snake. It is fast and computationally lightweight because it employs cubic splines instead of a deep neural network as a basis function. Its training parameters are physically interpretable because they directly represent control knots of the segmentation curve. Traditional snakes involve minimization of the edge-based loss functionals by deriving the Euler-Lagrange equation followed by its numerical solution. However, PICS directly minimizes the loss functional, bypassing the Euler Lagrange equations. It is the first snake variant to minimize a region-based loss function instead of traditional edge-based loss functions. PICS uniquely models the three-dimensional (3D) segmentation process with an unsteady partial differential equation (PDE), which allows accelerated segmentation via transfer learning. To demonstrate its effectiveness, we apply PICS for 3D segmentation of the left ventricle on a publicly available cardiac dataset. While doing so, we also introduce a new convexity-preserving loss term that encodes the shape information of the left ventricle to enhance PICS's segmentation quality. Overall, PICS presents several novelties in network architecture, transfer learning, and physics-inspired losses for image segmentation, thereby showing promising outcomes and potential for further refinement

Differential regulation of intracellular factors mediating cell cycle, DNA repair and inflammation following exposure to silver nanoparticles in human cells

10.1186/2041-9414-3-2Genome Integrity3

Effect of fish meal extract spray on the yield of Co-47 rice variety

A field experiment was conducted with Co-47 rice variety at Annamalai University experimental farm from July to September 2018 with 12 treatments to evolve a suitable organic and inorganic preparations foliar spray along with the application of 75 and 100% recommended NPK + humic acid granules. The results revealed that the treatment T6 - 100% NPK + humic acid granules @12.5 kg/ha+ Panchagavya + fish meal extract + Auxin Gold seaweed extract spray on 20, 35, and 50 DAT ranked first in terms of tiller number/m2 (385), number of filled grains/panicle (105.42), panicle length (23.99), and grain yield (5660). Among the individual organic preparations foliar spray, fish meal extract spray was found to be better and improved the grain yield to the tune of 439 and 387 kg/ha over respective 100 and 75% NPK + humic acid granules @12.5 kg/ha application

Effect of fish meal extract spray on the yield of Co-47 rice variety

116-121A field experiment was conducted with Co-47 rice variety at Annamalai University experimental farm from July to September 2018 with 12 treatments to evolve a suitable organic and inorganic preparations foliar spray along with the application of 75 and 100% recommended NPK + humic acid granules. The results revealed that the treatment T6 - 100% NPK + humic acid granules @12.5 kg/ha+ Panchagavya + fish meal extract + Auxin Gold seaweed extract spray on 20, 35, and 50 DAT ranked first in terms of tiller number/m2 (385), number of filled grains/panicle (105.42), panicle length (23.99), and grain yield (5660). Among the individual organic preparations foliar spray, fish meal extract spray was found to be better and improved the grain yield to the tune of 439 and 387 kg/ha over respective 100 and 75% NPK + humic acid granules @12.5 kg/ha application

A Regions of Confidence Based Approach to Enhance Segmentation with Shape Priors

©2010 SPIE - Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic electronic or print reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Presented at Computational Imaging VIII, January 17, 2010, San Jose, CA.http://dx.doi.org/10.1117/12.850888We propose an improved region based segmentation model with shape priors that uses labels of confidence/interest to exclude the influence of certain regions in the image that may not provide useful information for segmentation. These could be regions in the image which are expected to have weak, missing or corrupt edges or they could be regions in the image which the user is not interested in segmenting, but are part of the object being segmented. In the training datasets, along with the manual segmentations we also generate an auxiliary map indicating these regions of low confidence/interest. Since, all the training images are acquired under similar conditions, we can train our algorithm to estimate these regions as well. Based on this training we will generate a map which indicates the regions in the image that are likely to contain no useful information for segmentation. We then use a parametric model to represent the segmenting curve as a combination of shape priors obtained by representing the training data as a collection of signed distance functions. We evolve an objective energy functional to evolve the global parameters that are used to represent the curve. We vary the influence each pixel has on the evolution of these parameters based on the confidence/interest label. When we use these labels to indicate the regions with low confidence; the regions containing accurate edges will have a dominant role in the evolution of the curve and the segmentation in the low confidence regions will be approximated based on the training data. Since our model evolves global parameters, it improves the segmentation even in the regions with accurate edges. This is because we eliminate the influence of the low confidence regions which may mislead the final segmentation. Similarly when we use the labels to indicate the regions which are not of importance, we will get a better segmentation of the object in the regions we are interested in