Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy (1908.03636v2)

Abstract: Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects. Although many automated methods for these tasks exist, they often struggle for images with low signal-to-noise ratios and/or dense packing of nuclei. It was recently shown for 2D microscopy images that these issues can be alleviated by training a neural network to directly predict a suitable shape representation (star-convex polygon) for cell nuclei. In this paper, we adopt and extend this approach to 3D volumes by using star-convex polyhedra to represent cell nuclei and similar shapes. To that end, we overcome the challenges of 1) finding parameter-efficient star-convex polyhedra representations that can faithfully describe cell nuclei shapes, 2) adapting to anisotropic voxel sizes often found in fluorescence microscopy datasets, and 3) efficiently computing intersections between pairs of star-convex polyhedra (required for non-maximum suppression). Although our approach is quite general, since star-convex polyhedra include common shapes like bounding boxes and spheres as special cases, our focus is on accurate detection and segmentation of cell nuclei. Finally, we demonstrate on two challenging datasets that our approach (StarDist-3D) leads to superior results when compared to classical and deep learning based methods.

• The paper presents a novel neural network framework that predicts radial distances to form star-convex polyhedra for accurate 3D segmentation.
• It leverages Fibonacci lattices and scaling factors to efficiently manage parameter demands and address anisotropic voxel sizes in microscopy datasets.
• Empirical results demonstrate improved detection over advanced methods, maintaining robust performance even on low signal-to-noise images.

Insights on Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy

The paper presents a novel methodology aimed at enhancing the accuracy of 3D object detection and segmentation in fluorescence microscopy, specifically targeting the segmentation of densely packed and often low signal-to-noise cell nuclei. The research builds upon an earlier 2D approach, which utilized star-convex polygons, by applying and expanding the concept to 3D with star-convex polyhedra.

Methodological Innovations

The core innovation lies in the development of a neural network framework that predicts radial distances from each pixel within a cell nucleus to its boundary in a set of directions forming a star-convex polyhedron. This representation aids in delineating cell nuclei with varying shapes, including common bounding geometries like boxes and spheres. The authors address several technical challenges:

1. Parameter Efficiency: The transition from 2D polygons to 3D polyhedra poses computational and representational challenges, particularly concerning the memory and processing demands. By leveraging Fibonacci lattices to determine the directions of rays for the radial distances, this methodology achieves an optimal balance, employing only 64 directions as a compromise between fidelity and computational feasibility.
2. Adaptation to Anisotropic Voxel Sizes: The anisotropy inherent in many microscopy datasets, where voxel sizes differ across dimensions, complicates accurate shape representation. The authors introduce scaling factors derived from the dataset's properties to normalize this anisotropy, allowing for more accurate modeling of cell shapes.
3. Efficient Non-Maximum Suppression (NMS): Ensuring that only one representation per nucleus is retained requires evaluating intersections of numerous star-convex polyhedra. The methodology incorporates a hierarchical bounding strategy, progressively tightening the intersection bounds from bounding spheres to convex hulls and rasterization as necessary, significantly enhancing the computational efficiency of the NMS step.

Empirical Results

The empirical evaluation employs two challenging microscopy datasets: one near isotropic, and the other highly anisotropic. Compared against classical watershed techniques and more recent approaches like U-Net, the proposed method demonstrates superior accuracy across a range of intersection over union (IoU) thresholds. Particularly notable is its performance on lower signal-to-noise images, a testament to its robustness in difficult conditions typical in biomedical datasets.

Additionally, the paper underscores the model's ability to maintain accuracy even with reduced training data, highlighting its practicality in real-world applications where data annotation can be resource-intensive.

Implications and Future Directions

This research holds significant implications for biomedical imaging, where precise cell nucleus detection and segmentation are foundational for subsequent tasks like cell tracking and lineage tracing. The methodology provides a powerful tool for advancing automated analysis in microscopy, paving the way for more nuanced studies of cellular interactions and behaviors in complex biological systems.

Future developments could explore extending this framework to other volumetric imaging domains and further refining the model for different types of cells or imaging conditions. Additionally, integrating this approach with real-time imaging systems could propel its utility in dynamic studies, offering real-time insights into cellular processes.

Overall, this work contributes a significant advancement in computational methods for biological imaging, reinforcing the role of tailored deep learning approaches in overcoming specific challenges presented by complex, noisy volumetric data.