Abstract TMP54: Development and testing of a fully automated tool for the detection, segmentation, and characterization of cervical carotid atherosclerotic disease
Bibliographic record
Abstract
Background: Rapid, accurate diagnosis and characterization of carotid atherosclerosis can help prevent disabling strokes. Although carotid plaques can be identified on CT angiography (CTA), interpretation is challenging for frontline physicians. Quantification of plaque volume/composition requires much manual effort. We developed and tested a fully automated tool for segmenting carotid lumens and delineating atherosclerotic plaque, distinguishing between calcific and hypodense components. Methods: We used 528 consecutive cases with CTA head/neck from a population of 7,745 patients with ischemic stroke and transient ischemic attack in an entire province (Alberta) presenting from 1-April-2016 to 31-March-2017. Trained readers supervised by a radiologist manually segmented right and left carotid artery lumens from 3 vertebral bodies below the bulb to 3 above, and segmented regions of carotid atherosclerotic plaque, regardless of degree of stenosis, separately labelling calcific and hypodense components. Cases were split 80/20 between training and testing datasets. The fully automated pipeline included coarse-scale detection of regions of interest, a two-stream U-shaped network for detection and segmentation of lumens and plaques, and a geometry-based inference algorithm to distinguish left and right labels. We evaluated plaque detection using diagnostic performance measures and segmentation using the Dice coefficient. Results: 422 cases were used for training and 106 for testing. In testing data, the fully automated tool achieved excellent performance for bilateral segmentation of carotid lumens (Dice 0.91, 95%CI:0.90-0.92, e.g. Figure 1 ). For detection of calcific and hypodense plaque components, respectively, the model achieved sensitivity of 96.5% (95%CI:89.3-99.1%) and 97.3% (89.6-99.5%), specificity of 95.2% (74.1-99.8%) and 75.8% (57.4-88.3%), positive predictive value of 98.8% (92.6-99.9%) and 89.9% (80.5-95.2%), negative predictive value of 87.0% (65.3-96.6%) and 92.6% (74.2-98.7%), and accuracy of 96.2% (90.1-98.8%) and 90.6% (82.9-95.1%, e.g. Figure 2 ). Dice score was 0.73 (0.69-0.76) for calcific plaque and 0.53 (0.49-0.57) for hypodense plaque. Conclusions: Our fully automated tool achieved good performance for detection and segmentation of carotid plaques. Calcific and hypodense plaque volumes can be automatically generated from these labels. Efforts are underway to further optimize the specificity and segmentation performance for hypodense plaques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".