docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-prenatal-neurology
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title: Prenatal Neurology
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Fetal Neurology, Prenatal Neuroscience]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.85
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verification_status: applied
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tags: [neurology, fetal-medicine, neurodevelopment, medical-imaging]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: MONAI / nnU-Net / 3D Slicer
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---
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# Prenatal Neurology
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## 매 한 줄
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> **"매 fetal nervous system 의 development, imaging, anomaly detection — neural tube 부터 birth 까지"**. 1980s ultrasound 의 advent 로 시작, 2010s fetal MRI 로 detail 폭증, 2020s deep learning 으로 automated segmentation/screening. 2026 currently SVRTK + diffusion priors 로 motion-corrected fetal MRI volumes 를 minutes 안에.
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## 매 핵심
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### 매 developmental milestones
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- **Week 3-4**: neural plate → neural tube closure. Failure → spina bifida, anencephaly.
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- **Week 5-7**: 3 primary vesicles → 5 secondary (telencephalon, diencephalon, mesenc, metenc, myelenc).
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- **Week 8-16**: neuronal proliferation in ventricular zone.
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- **Week 12-22**: neuronal migration along radial glia. Failure → lissencephaly, heterotopia.
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- **Week 22-40**: gyrification, cortical organization, myelination begins.
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### 매 imaging modalities
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- **Ultrasound (US)**: routine 18-22 wk anatomy scan; transvaginal early.
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- **Fetal MRI**: T2-HASTE / SSFSE; problem-solving when US ambiguous.
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- **Doppler**: middle cerebral artery flow (anemia, hypoxia).
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- **Fetal MEG / EEG**: research only.
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### 매 common anomalies
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1. **Neural tube defects** (NTDs): spina bifida, anencephaly. Folate-preventable.
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2. **Ventriculomegaly**: atrial width >10mm.
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3. **Corpus callosum agenesis**: 1:4000.
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4. **Posterior fossa**: Dandy-Walker, Blake's pouch cyst.
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5. **Cortical malformations**: lissencephaly, polymicrogyria.
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6. **TORCH infections**: CMV, Zika → microcephaly, calcifications.
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### 매 AI in fetal imaging (2024-2026)
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- **SVRTK / NiftyMIC**: slice-to-volume reconstruction from motion-corrupted MRI.
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- **nnU-Net fetal**: automatic brain extraction + tissue segmentation.
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- **dHCP atlas**: developing Human Connectome Project — gestational-age-specific atlas.
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- **Diffusion priors**: latent diffusion models for fetal MRI super-resolution (2024-2025).
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- **Automated biometry**: BPD, HC, AC, FL from US in real time (e.g., Caption Health-style).
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## 💻 패턴
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### Fetal brain extraction (nnU-Net)
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```python
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# Train on FeTA Challenge dataset (gestational ages 20-35 wk)
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# nnU-Net handles preprocessing, augmentation, ensemble
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import subprocess
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subprocess.run([
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"nnUNetv2_train", "Dataset080_FetalBrain", "3d_fullres", "0",
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"--npz",
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])
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# Inference
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subprocess.run([
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"nnUNetv2_predict",
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"-i", "input_dir", "-o", "output_dir",
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"-d", "080", "-c", "3d_fullres", "-f", "0",
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])
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```
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### Slice-to-volume reconstruction (SVRTK)
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```bash
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# Motion-corrupted T2 stacks → isotropic 3D volume
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mirtk reconstruct recon.nii.gz \
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4 stack_axi.nii.gz stack_cor.nii.gz stack_sag.nii.gz stack_obl.nii.gz \
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-mask brain_mask.nii.gz \
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-resolution 0.5 \
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-iterations 3 \
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-thickness 3.0 3.0 3.0 3.0
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```
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### Tissue segmentation w/ MONAI
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```python
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import torch
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from monai.networks.nets import SwinUNETR
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from monai.transforms import Compose, LoadImaged, NormalizeIntensityd, EnsureChannelFirstd
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model = SwinUNETR(img_size=(96, 96, 96), in_channels=1, out_channels=8,
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feature_size=48, use_checkpoint=True)
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model.load_state_dict(torch.load("feta_swinunetr.pt"))
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# Outputs: CSF, GM, WM, ventricles, cerebellum, brainstem, deep GM, hippocampus
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```
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### Gestational-age-specific atlas registration
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```python
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# dHCP: 36 atlases from 28-44 weeks PMA
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import ants
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fixed = ants.image_read(f"dhcp_atlas/week_{ga_weeks}.nii.gz")
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moving = ants.image_read("fetal_brain_recon.nii.gz")
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reg = ants.registration(fixed, moving, type_of_transform="SyN")
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warped = reg["warpedmovout"]
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```
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### Automated US biometry (real-time)
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```python
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# YOLOv8 finds standard plane → keypoint regression for BPD/HC/AC/FL
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from ultralytics import YOLO
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plane_model = YOLO("us_plane_classifier.pt")
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biometry = YOLO("us_keypoints.pt")
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res = plane_model(frame)
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if res[0].names[res[0].probs.top1] == "axial_thalami":
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pts = biometry(frame)[0].keypoints
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bpd_mm = euclidean(pts[0], pts[1]) * pixel_spacing
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```
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### Cortical folding metric (gyrification index)
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```python
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# GI = total surface area / convex hull area (per hemisphere)
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import nibabel as nib, numpy as np
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from skimage.measure import marching_cubes, mesh_surface_area
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seg = nib.load("cortex.nii.gz").get_fdata() > 0
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verts, faces, _, _ = marching_cubes(seg, level=0.5)
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surf = mesh_surface_area(verts, faces)
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# Convex hull surface
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from scipy.spatial import ConvexHull
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hull = ConvexHull(verts)
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gi = surf / hull.area
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| Routine screening 18-22 wk | Ultrasound (anatomy scan) |
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| Suspected CNS anomaly on US | Fetal MRI (32-34 wk optimal) |
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| Motion-corrupted MRI | SVRTK reconstruction |
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| Quantitative volumetry | dHCP atlas + nnU-Net |
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| Suspected NTD | High-resolution US + AFP + acetylcholinesterase |
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**기본값**: US first; MRI for problem-solving; AI segmentation for research/quantitative endpoints.
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## 🔗 Graph
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## 🤖 LLM 활용
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**언제**: fetal imaging analysis, neurodevelopmental research, congenital anomaly screening pipelines.
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**언제 X**: clinical diagnosis without licensed clinician — AI augments, never replaces.
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## ❌ 안티패턴
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- **Adult MRI tools on fetal data**: gestational-age-specific contrast / atlas required.
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- **Ignoring motion artifact**: fetal motion → reconstruct first.
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- **No GA stratification**: 24wk vs 36wk brain are different organs.
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- **Single-modality conclusion**: combine US + MRI + genetics.
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- **Overcalling ventriculomegaly**: 10-12mm often resolves; counsel carefully.
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## 🧪 검증 / 중복
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- Verified (FeTA Challenge MICCAI, dHCP, ISUOG guidelines, AIUM practice parameters).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — fetal neurodevelopment + AI imaging stack |
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