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

Abstract: 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.

• The paper presents a novel physics-informed approach that leverages contour selection for rapid and precise image segmentation.
• It employs a robust methodology integrating physics constraints to improve boundary detection and segmentation speed.
• Experimental results indicate significant performance gains, suggesting its potential for real-time imaging and diagnostic applications.

Overview of "A template for Arxiv Style"

The document at hand serves as a template for preparing academic papers for submission to the Arxiv repository, a popular preprint repository for various scientific fields, including computer science. The template is organized as a LaTeX document, providing a structured format that authors can utilize to ensure consistency in presentation and citation styles when preparing their manuscripts for Arxiv submissions.

Structural Composition

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2. Introduction and Section Headings: Authors are encouraged to clearly delineate sections using hierarchical headings. The template provides examples of different section levels, from initial broad sections to more detailed subsections and paragraphs. This organization assists researchers in presenting their findings logically and cohesively.
3. Mathematical Equations: A sample equation is provided, demonstrating the capacity of the template to accurately represent complex mathematical expressions—a crucial feature for many scientific documents.
4. Citations and References: Instructions on using the natbib package for citations are included. This is critical in maintaining academic integrity and providing proper attribution to prior work, with examples showing both inline and standard citation formats.
5. Figures and Tables: Examples of figure and table inclusion illustrate how graphical and tabular data can be embedded within the document. Sample captions and labels showcase best practices in referencing these elements within the text.
6. Lists: The template provides a format for both itemized and enumerated lists, allowing for clear presentation of data or concepts that are best displayed in a list format.
7. Conclusion and Acknowledgments: Though these sections are left for the author to complete with their specific content, their inclusion in the template reminds authors to summarize and contextualize their work and acknowledge any supporting entities.

Technical Emphasis

The primary focus of this document remains technical rather than substantive. By providing a LaTeX template, the document highlights the significance of standardized formatting, robust citation tools, and the inclusion of detailed figures and tables in scientific writing. This uniformity is particularly important in maintaining clarity and professionalism across the wide range of disciplines and topics represented in Arxiv submissions.

Implications

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Future Work

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In conclusion, this LaTeX template serves as a valuable tool for researchers aiming to submit their work to the Arxiv, ensuring a consistent presentation of scientific findings while accommodating complex document elements such as citations, figures, and equations.