Automated Segmentation of Complex Biological Structures in Histopathology Images
Bibliographic record
Abstract
Analysis of high-resolution histopathology whole slide images (WSIs) is a vital step in diagnosis and treatment of many diseases, including placenta-mediated diseases (PMDs) and respiratory illnesses. Currently, the most trusted approach is manual/semi-automated analysis of histopathology WSIs by an expert pathologist. This is problematic because high-resolution histopathology WSIs usually have a very large size (e.g., 80,000×80,000 pixels) and a large number of complex biological structures. As such, applying manual/semi-automated approaches to assess histopathology WSIs can be inefficient, expensive, and subject inter- and intra-rater variability. An alternative approach to manual and semi-automated approaches is to implement machine learning and image processing techniques to develop automated histopathology image analysis (AHIA) pipelines. A fundamental step in generating accurate AHIA approaches is semantic segmentation of biological structures in high-resolution histopathology images. In this thesis, our main objective is to develop accurate AHIA pipelines for semantic segmentation of complex biological structures in histopathology WSIs. We specifically focus on two histopathology applications: 1) segmentation of villi in histopathology WSIs of human placenta and 2) segmentation of complex biological structures of mouse lung tissue. Initially, we investigate the rule-based methods using conventional machine learning and image processing methods for segmentation of histopathology WSIs, developing two separate pipelines to address the challenges associated with each of our applications. We demonstrate that rule-based AHIA approaches show promising performance for analysis of histopathology WSIs in each of applications in comparison to manual assessment by expert pathologists and can be considered as a potential replacement for manual/semi-automated approaches. Then, we investigate deep learning methods in semantic segmentation of histopathology WSIs to further improve our developed rule-based approaches in terms of segmentation performance, generalizability, and training and testing speed. One of the bottlenecks in developing deep learning methods is the large size of histopathology WSIs, which requires implementing a patch-based approach to feed the images to deep learning models. We demonstrate that this bottleneck may limit the performance of the deep learning models due to three-fold trade-off between field-of-view, computational efficiency, and spatial resolution. As such, we propose a multi-resolution semantic segmentation pipeline to address this trade-off in AHIA using deep learning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".