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Cascade Detector Analysis and Application to Biomedical Microscopy

Thomas L. Athey, Shashata Sawmya, Nir Shavit

TL;DR

This work addresses the challenge of efficient inference on massive, multiresolution biomedical images in which objects of interest are sparse. It introduces a probabilistic theory of cascade detectors operating across resolutions, deriving closed-form expressions for cascade sensitivity, specificity, and expected L0 classifier calls, and generalizes these results to higher dimensions and more cascade levels. Empirically, the authors demonstrate that two-level cascade detectors achieve comparable accuracy to single-level detectors while delivering substantial speedups (typically 2×–4×) across soma detection, organelle segmentation, and tissue segmentation tasks on fluorescence, EM, and digital pathology datasets. The findings suggest broad applicability of cascade inference in microscopy and potential for combining with other efficiency techniques such as sparsification and quantization to accelerate large-scale biomedical image analysis.

Abstract

As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently identify sparse objects in multiresolution images. Given an object's prevalence and a set of detectors at different resolutions with known accuracies, we derive the accuracy, and expected number of classifier calls by a cascade detector. These results generalize across number of dimensions and number of cascade levels. Finally, we compare one- and two-level detectors in fluorescent cell detection, organelle segmentation, and tissue segmentation across various microscopy modalities. We show that the multi-level detector achieves comparable performance in 30-75% less time. Our work is compatible with a variety of computer vision models and data domains.

Cascade Detector Analysis and Application to Biomedical Microscopy

TL;DR

This work addresses the challenge of efficient inference on massive, multiresolution biomedical images in which objects of interest are sparse. It introduces a probabilistic theory of cascade detectors operating across resolutions, deriving closed-form expressions for cascade sensitivity, specificity, and expected L0 classifier calls, and generalizes these results to higher dimensions and more cascade levels. Empirically, the authors demonstrate that two-level cascade detectors achieve comparable accuracy to single-level detectors while delivering substantial speedups (typically 2×–4×) across soma detection, organelle segmentation, and tissue segmentation tasks on fluorescence, EM, and digital pathology datasets. The findings suggest broad applicability of cascade inference in microscopy and potential for combining with other efficiency techniques such as sparsification and quantization to accelerate large-scale biomedical image analysis.

Abstract

As both computer vision models and biomedical datasets grow in size, there is an increasing need for efficient inference algorithms. We utilize cascade detectors to efficiently identify sparse objects in multiresolution images. Given an object's prevalence and a set of detectors at different resolutions with known accuracies, we derive the accuracy, and expected number of classifier calls by a cascade detector. These results generalize across number of dimensions and number of cascade levels. Finally, we compare one- and two-level detectors in fluorescent cell detection, organelle segmentation, and tissue segmentation across various microscopy modalities. We show that the multi-level detector achieves comparable performance in 30-75% less time. Our work is compatible with a variety of computer vision models and data domains.
Paper Structure (11 sections, 1 theorem, 4 equations, 3 figures, 3 tables)

This paper contains 11 sections, 1 theorem, 4 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Theoretical Motivation
  4. Experiments
  5. Datasets
  6. Soma detection in fluorescence images
  7. Organelle segmentation in electron microscopy
  8. Tissue segmentation in digital pathology
  9. Implementation
  10. Results
  11. Discussion

Key Result

proposition thmcounterproposition

Under the setting described above, the two level cascade detector has true positive rate $\beta_{1,0}$ and false positive rate $\alpha_{1,0}$, where Additionally, if $K_n$ is the number of calls to the L0 classifier, then

Figures (3)

  • Figure 1: Cascade detectors use different levels of a multiresolution image pyramid during inference. a) Schematic of a single-level detector in microscopy where a large image is broken into chunks due to memory limitations. b) In a cascade detector, low resolution data is processed first to rapidly rule out background regions. Lower level detectors are only called on positive candidates. Pictured is a subset of the fluorescence microscopy dataset from Bloss et al. bloss_structured_2016.
  • Figure 2: The performance of the cascade detector depends on the accuracies of the detectors at both levels, and the prevalence of the object of interest. We performed a sensitivity analysis of these parameters by setting them to a set of values ($\beta_0=0.85$, $\beta_1=0.8$, $\alpha_0=0.05$, $\alpha_1=0.1$, $0=0.1$), then individually varying $p$ (b), $\beta_1$ (c) and $1-\alpha_1$ (d). The detectors' sensitivity, specificity, precision, and expected number of calls to any classifier (normalized by the number of L0 chunks) are plotted.
  • Figure 3: A selection of results on the various datasets. a The test image from the CA1 somas dataset included seven fluorescent neurons bloss_structured_2016 with insets showing the detections from the cascade detector (red points) and single-level detector (blue points). b The test image from the fMOST somas dataset includes fluorescent neurons throughout mouse cortex zeng_whole_2024. Insets show four neurons that were correctly detected by both the cascade detector (red points) and single-level detector (blue points). c A slice of the test image from the C. elegans mitochondria dataset are shown, with the ground truth segmentation overlaid in green, and the outer limits of the volume shown in blue witvliet_connectomes_2021. Red grid lines depict the L1 subvolumes that were passed to the L0 detector. d A slice of the HeLa nucleus test set is shown, with the same overlays as in c. e-h Two test images from the H&E tissue dataset are shown (e,g), with the detector segmentations (f,h). The tissue segmentation by the single-level detector (green) and cascade detector (magenta) are summed in color space where they overlap (white).

Theorems & Definitions (4)

  • proposition thmcounterproposition
  • proof
  • remark thmcounterremark
  • remark thmcounterremark