Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)
Bibliographic record
Abstract
A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".