Three Different Strategies for Real-Time Prostate Capsule Volume Computation from 3-D End-Fire Transrectal Ultrasound
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
estimation of prostate capsule volume via segmentation of the prostate from 3-D ultrasound volumetric ultrasound images is a valuable clinical tool, especially during biopsy. Normally, a physician traces the boundaries of the prostate manually, but this process is tedious, laborious, and subject to errors. The prostate capsule edge is computed using three different strategies: (a) least square approach, (b) level set approach, and (c) Discrete Dynamic Contour approach. (a) In the least square method, edge points are defined by searching for the optimal edge based on the average signal characteristics. These edge points constitute an initial curve which is later refined; (b) Level set approach. The images are modeled as piece-wise constant, and the energy functional is defined and minimized. This method is also automated; and (c) The Discrete Dynamic Contour (DDC). A trained user selects several points in the first image and an initial contour is obtained by a model based initialization. Based on this initialization condition, the contour is deformed automatically to better fit the image. This method is semi-automatic. The three methods were tested on database consisting of 15 prostate phantom volumes acquired using a Philips ultrasound machine using an end-fire TRUS. The ground truth (GT) is developed by tracing the boundary of prostate on a slice-by-slice basis. The mean volumes using the least square, level set and DDC techniques were 15.84 cc, 15.55 cc and 16.33 cc, respectively. We validated the methods by calculating the volume with GT and we got an average volume of 15.
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.
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 it