Optimal planar X-ray imaging soft tissue segmentation using a photon counting detector
Bibliographic record
Abstract
A rigorous method for automated soft tissue segmentation using planar kilovoltage (kV) imaging, a photon counting detector (PCD), and a convolutional neural network is presented. The goal of the project was to determine the optimum number of energy bins in a PCD for soft tissue segmentation. Planar kV X-ray images of solid water (SW) phantoms with varying depth of cartilage were generated with a cone-beam analytical method and parallel-beam Monte Carlo simulations. Simulations were preformed using 2 to 5 PCD energy bins with equal photon fluence distribution. Simulated image signal to noise ratio (SNR) was varied between 10 to 250 measured after transmission through 4 cm of SW. Algorithms using non-linear as well as linear regression were used to predict the amount of cartilage for every pixel of the phantom. These algorithms were evaluated based on the mean squared error (MSE) between their prediction and the ground truth. The best algorithm was used to decompose randomly generated SW and cartilage images with an SNR of 100. These randomly generated images trained a U-Net convolutional neural network to segment the cartilage in the image. The results indicated the smallest MSE occurred for non-linear regression with 4 energy bins over all SNR. The trained U-Net was able to correctly segment all regions of cartilage for the smallest amount of cartilage used (4 mm) and segmented the region with > 99% categorical accuracy by pixel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".