45 件 ・ 関連度順
概要と使いどころ
Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.
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Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.
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Use Dipy for diffusion MRI IO, preprocessing, reconstruction, tractography, registration, visualization-aware workflows, and command-line operations.
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Think and work like an expert Medical Physicist. Use when a task calls for Medical Physicist judgment. Reasons from CTDIvol/SSDE, HU accuracy, ACR/MQSA QA, TG-126 PET/CT, TG-18/TG-270 displays, and NCRP 147 shielding across CT, MRI, mammography, NM/PET, and therapy QA while treating phantom-mismatch dose claims, SUV normalization drift, display washout, and post-upgrade MEE gaps as first-class failure modes.
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Think and work like an expert Anatomist. Use when a task calls for Anatomist judgment. Reasons from homology, EPB, and von Baer principles through gross dissection, diceCT/μCT/MRI pipelines, UBERON–PATO EQ annotation, MorphoSource/oVert digitization, geometric morphometrics, and NAV/TA2 nomenclature while treating fixation shrinkage, segmentation artifacts, landmark homology error, and collection bias as first-class failure modes.
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Evaluates neuromarketing studies and neural-data law. Use when judging EEG, fMRI, eye-tracking, or GSR ad study or vendor, or if signals are regulated neural data.
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Use this skill when working with Brain Imaging Data Structure (BIDS) datasets: organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
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This skill recommends re-evaluating the need for treatment of other pituitary hormone deficiencies in the postoperative period following surgical intervention for Cushing's syndrome. It is triggered when assessing a post-op CS patient for ongoing pituitary function, evaluating for hypopituitarism, or checking HPA axis recovery with morning cortisol/ACTH stimulation tests and pituitary MRI within 1–3 months.
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Suggests repeat transsphenoidal surgery for patients with persistent hypercortisolism after initial transsphenoidal surgery when imaging shows evidence of incomplete resection or a pituitary lesion. Clinical triggers include postoperative MRI revealing residual adenoma, persistent elevated UFC or late-night salivary cortisol, and visible pituitary lesion on imaging.
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This skill recommends localizing and resecting ectopic ACTH-secreting tumors with node dissection when ectopic ACTH secretion is suspected or confirmed. Clinical triggers include unexplained persistent hypercortisolism after pituitary surgery, elevated ACTH with negative pituitary MRI, or identification of occult ectopic ACTH syndrome (EAS) on biochemical testing.
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This skill recommends transsphenoidal selective adenomectomy (TSS) by an experienced pituitary surgeon as the optimal treatment for Cushing's disease in pediatric and adult patients. Use when a patient with ACTH-dependent Cushing syndrome is a surgical candidate; triggers include overt Cushing's syndrome, elevated UFC or late-night salivary cortisol, and pituitary MRI showing an adenoma.
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This skill recommends computed tomography (CT) as the initial imaging modality for primary aldosteronism (PA) evaluation in Japan, citing its accessibility and comparable performance to MRI. It is triggered when a clinician orders imaging for suspected PA and asks 'What imaging should I start with?' or seeks a cost-effective initial evaluation.
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Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.
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Re-run the analysis pipeline on all runs matching an RQ, reaggregate metrics, and propose findings updates against the fresh data. Trigger when the user says "reanalyze RQ-N", "reanalyse RQ-N", "Runs neu analysieren", or wants to refresh metrics/findings after a pipeline change (analyze-run.sh fix, adapter added, ESLint config).
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TDD Green Phase - Implement minimal code to make the failing test pass
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TDD Red Phase - Activate ONE test from the test list and make it fail with explicit predictions
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Generates an experiment-overview snapshot of all research questions under research/reports/. Invoke when a new point-in-time report across all RQs should be produced.
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Use this skill to drive a research question (RQ) end-to-end: validate the RQ README, generate a fill batch-plan, start the Docker batch in the background, monitor progress, run aggregation, and propose findings updates. Trigger when the user says "RQ-N voranbringen", "run-rq", "fill RQ-N", "run RQ-N", "Forschungsfrage N starten", or names a specific RQ-N directory in research/.
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TDD Refactor Phase - Improve code while keeping tests green using Simple Design Rules and APP mass
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TDD Test List Phase - Create a comprehensive test list covering every example and rule from the specification
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Knowledge base from the NASA Digital Engineering Acquisition Framework Handbook (NASA-HDBK-1004). Use for acquiring digital/model-based data under contract: the Data Requirements Description (DRD) instrument and the Appendix A DRD suite (TDP, MBx, CDMP, CSA, CR, FCA/PCA, SDT, EMDAL, REF, MRI); the Data Type 1-5 control scale; SOW/RFP/Cost-Volume contract language and data rights (FAR/DFARS, Table 3 license categories); the Requirements Exchange Format (REF/ReqIF, UUID schema) and Master Records Index; model-based data definition (MBSE, CAD/PMI, M&S credibility, BOM, release-state mapping, PLM); digital data collaboration, the four owned architectures, and interoperability standards (MIL-STD-31000, STEP/ISO 10303, ASME Y14); the four MBE interoperability use cases; and MBE Plan development. Scope is the acquisition and contracting side of digital engineering. Does not reproduce ISO/STEP/ASME/MIL-STD/SAE standard text, and the companion NASA-HDBK-1009A carries the deeper digital-engineering practitioner detail this handbook defers to.
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