Correction of brain shift with Intraoperative Ultrasound - segmentation challenge
Bibliographic record
Abstract
Early brain tumor resection can effectively improve the patient’s survival rate (Jakola et al. 2012). The proposed challenge intends to solve key issue in ultrasound-guided brain tumor resection, which is to outline the tumor boundary in intra-operative ultrasound. The tumor segmentation will (1) assist the surgeon in image interpretation, especially after brain shift has occurred and the MRI is no longer reliable and (2) segmented structures could be used to drive registration algorithms to compensate for brain shift. Intra-operative ultrasound (iUS) is a robust and relatively inexpensive technique to track intra-operative tissue shift and surgical tools. Automatic algorithms for brain tissue segmentation in iUS, especially brain tumors and resection cavity can greatly facilitate the robustness and accuracy of brain shift correction through image registration, and allow easy interpretation of the iUS. This has the potential to improve surgical outcomes and patient survival rate (Xiao et al. 2020). The challenge is an extension to the previous CuRIOUS 2018&2019 Challenge that focused on image registration algorithms. It will provide a snapshot of the current and new techniques for iUS segmentation which may further enhance the usefulness of ultrasound in surgical guidance by assisting the neurosurgeon in ultrasound image interpretation as well as input to image registration algorithms. The challenge will provide the opportunity to benchmark the methods on the newly released dataset of iUS brain tumor and resection cavity segmentation. References Jakola et al. 2012: Comparison of a strategy favoring early surgical resection vs a strategy favoring watchful waiting in low-grade gliomas. JAMA, 2012 Gerard et al. 2017: Brain shift in neuronavigation of brain tumors: A review, Medical Image Analysis, 2017. Munkvold et al. 2017: Tumor volume assessment in low-grade gliomas: A comparison of preoperative magnetic resonance imaging to coregistered 3-dimensional ultrasound recordings, Neurosurgery, Aug 2017. Xiao et al. 2017: Retrospective evaluation of cerebral Tumors (RESECT): A clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries, Medical Physics, Apr. 2017 Maier-Hein et al. 2018: Why rankings of biomedical image analysis competitions should be interpreted with care, Nature Communications, 2018 Xiao et al. 2020: Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS 2018 Challenge, IEEE TMI, 2020 Wiesenfarth et al. 2021: Methods and open-source toolkit for analyzing and visualizing challenge results, Scientific reports, 2021 Carton et al. (2020): Automatic segmentation of brain tumor resections in intraoperative ultrasound images using U-Net, Journal of Medical Imaging 7(3); doi: 10.1117/1.JMI.7.3.031503].
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".