# Longitudinal T1c Brain-Tumor Segmentation

Longitudinal (repeated-measures) analysis of brain T1 post-contrast (T1c) MRI with
tumor segmentation, plus an **iterative pseudo-labeling** study that leverages a
large unlabeled pool from the same subjects/series to improve a held-out,
patient-level test Dice.

All volumes are preprocessed to a uniform 1 mm isotropic, head-cropped,
percentile-normalized form so that tumor counts are directly comparable in mm³
across sources and timepoints.

## Environment

Conda env `longitudinal` (Python 3.14, torch 2.14 +cu126). Activate before any
command:

```bash
source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal
```

GPU (CUDA 12.6) is available; multi-GPU jobs use `torchrun` (in-house) or
nnU-Net's own DDP (`-num_gpus`). Run scripts from the repo root.

## Data sources

| Source | What | Notes |
|---|---|---|
| `ntuh` | Labeled T1c + tumor segmentation | Native MR, deduped per acquisition |
| `m6` | Labeled (GTV warped from CT) + large unlabeled pool | GTV registered CT→T1c |
| `lee` | Longitudinal T1c scanned as JPG + DICOM txt | Volumes reconstructed from slices |

## Directory layout

```
data/
  manifests/            jsonl row tables (see Pipeline A)
  proc/                 <key>.nii.gz (1mm, cropped, normalized)
  proc/<key>_label.nii.gz
  procmeta/<key>.json   geometry (origin/direction/crop_vox) + normalization
  vols.json             labeled tumor volume stats (mm3 percentiles)
  pseudo/roundK/        in-house pseudo-labels (rows.jsonl, masks, summary.json)
src/                    U-Net, dataset, losses, training/eval helpers
scripts/                Pipeline A (in-house 3D U-Net)
scripts_nnu/            Pipeline B (nnU-Net)
runs/roundK/            in-house checkpoints (best.pt, final.pt, state.pt)
runs_nnu/roundK/        nnU-Net checkpoint snapshots (best_nnu.pth)
nnu/                    nnU-Net raw / preprocessed / results trees
results/                evaluation JSON tables + plots
logs/                   run logs
```

---

## Pipeline A — In-house 3D U-Net (`scripts/`)

3-class-capable but used as binary (background / tumor) `Unet3D`, 96³ patches,
DDP, per-sample weighted loss.

| # | Script | Purpose |
|---|---|---|
| 01 | `01_build_ntuh_manifest.py` | Build NTUH2022G4 labeled T1c + seg manifest |
| 02 | `02_build_m6_dataset.py` | Build M6-2025 manifests (GTV CT→T1c registration/warp) |
| 03 | `03_scan_lee_t1c.py` | Scan lee for brain T1c series → raw + selected manifests |
| 04 | `04_reconstruct_lee.py` | Reconstruct lee T1c niftis from JPG slices + txt metadata |
| 05 | `05_build_splits.py` | Patient-level train/val/test splits + unlabeled pool + volume stats |
| 06 | `06_pseudo_label.py` | Pseudo-label the pool with a round model (sliding-window + TTA) |
| 07 | `07_train.py` | DDP training entrypoint (labeled + weighted pseudo rows) |
| 08 | `08_eval.py` | Held-out test evaluation (Dice from probability map) |
| 09 | `09_run_iterative.py` | Orchestrates rounds 0…K and produces the summary table/plot |
| — | `preprocess.py` | Crop/normalize source volumes → `data/proc/` |
| — | `scan_procs.py` | Flag corrupt/non-finite processed volumes |
| — | `test_dataloader.py` | Smoke-test the training dataloader |

Pseudo-label gates (per volume): **pos** when `p_tumor ≥ tau_pos`, largest-CC
fraction ≥ `min_cc_frac`, and volume within the labeled p2–p98 range; **neg** when
≥ `neg_frac` of interior voxels have `p_bg ≥ tau_neg`; then a per-subject
longitudinal consistency filter.

Run the full study:

```bash
python scripts/09_run_iterative.py --rounds 4 --gpus 3
```

Individual stages are standalone, e.g.:

```bash
torchrun --standalone --nproc_per_node 3 scripts/07_train.py \
    --rows data/manifests/split_train.jsonl --val data/manifests/split_val.jsonl \
    --epochs 40 --lr 3e-4 --batch 3 --ckpt-dir runs/round0
python scripts/08_eval.py --rows data/manifests/split_test.jsonl --ckpt runs/round0/best.pt
```

### Known issue in Pipeline A (longitudinal consistency)

`06_pseudo_label.py`'s `grid_info()`/`dice_a_on_b()` resample timepoints into a
shared physical space using `procmeta` origin/direction/crop_vox. This is
**not reliable across separate acquisitions**:

- Each scan has its own scanner/patient coordinate frame (table offset + head
  pose), so two visits of the same subject do not overlap in absolute space.
  Benchmarking against labeled multi-timepoint patients gives median cross-visit
  true-label Dice ≈ 0, meaning the consistency gate tends to over-reject.
- `crop_vox` is stored as `[z, y, x]` array-axis starts, while `direction` is
  ordered `(x_dir, y_dir, z_dir)`; the pairings in `grid_info` mix these up.

This is noted for the record; Pipeline B below replaces the gate with a
frame-invariant one. Pipeline A was left unchanged.

---

## Pipeline B — nnU-Net iterative pseudo-labeling (`scripts_nnu/`)

The same study driven by **nnU-Net v2** (installed build `nnunetv2` 2.8.1) as the
segmentation backbone, keeping the identical patient-level splits, unlabeled
pool, and evaluation protocol so results are directly comparable to Pipeline A.

> This installed nnU-Net is a modern fork: preprocessed data is `.b2nd`
> (blosc2), checkpoints store `network_weights` (not `model`), and
> `--save_probabilities` writes a per-case `<case>.npz` (channel-first
> `(C, z, y, x)`). The code below targets that build, not the upstream nnU-Net
> docs.

Dataset: `Dataset210_NTUH_T1C_PL`, single channel `t1c`, 2 classes
(background=0, tumor=1), `3d_fullres` only, `nnUNetPlans`, fold 0.

| # | Script | Purpose |
|---|---|---|
| 01 | `01_nnu_prepare_dataset.py` | Build/rebuild the raw dataset (symlinked `imagesTr`/`labelsTr` + `dataset.json`) from a rows jsonl |
| 02 | `02_nnu_plan_preprocess.py` | Plan + preprocess (`--clean`), then write subject-level `splits_final.json` |
| 03 | `03_nnu_train.py` | Train one round via `NTUHLPLTrainer` (DDP `-num_gpus`), optional warm start |
| 04 | `04_nnu_pseudo_label.py` | Multi-GPU `nnUNetv2_predict` on the pool + selection gates |
| 05 | `05_nnu_eval_test.py` | Held-out test evaluation (probability Dice@0.5 + hard-seg Dice) |
| 06 | `06_nnu_run_iterative.py` | Orchestrates rounds 0…K, table + plot |
| — | `trainers/ntuh_pl_trainer.py` | `NTUHLPLTrainer`: env-driven epochs/LR + full-weight warm start |
| — | `nnu_common.py` | Shared paths, env, dataset/split helpers, selection + consistency |

### Round semantics

- **Round 0:** train on the labeled patient-level train split (nnU-Net internal
  validation = the held-out val split, patient-disjoint via `splits_final.json`).
  Pseudo-label the remaining pool → `data/pseudo_nnu/round1`.
- **Round k (k ≥ 1):** dataset grows with all accepted pseudo-labels from rounds
  1…k (positives + zero-mask negatives, de-duplicated by key); re-plan/preprocess;
  **warm-start** training from round k−1's best checkpoint at a lower LR; predict
  the *remaining* pool; evaluate on test.

Because this build has no incremental preprocessing, each round re-plans and
re-preprocesses the whole (growing) dataset; the pool shrinks each round since
accepted cases are excluded from re-prediction.

### Custom trainer

`NTUHLPLTrainer` (resolved through the `nnUNet_extTrainer` env var) adds:

- `NNU_PL_EPOCHS` / `NNU_PL_LR` — epoch count and initial PolyLR (set per round).
- **Full-weight warm start** (`NNU_PL_WARMSTART`): loads *all* weights including
  the segmentation head in `on_train_start`. The CLI `-pretrained_weights` flag
  deliberately skips `.seg_layers.` keys, which would silently re-initialize the
  head and break round-to-round fine-tuning — hence this hook.

### Longitudinal consistency (frame-invariant)

Instead of resampling into absolute patient space (unreliable, see Pipeline A
note), two timepoints are compatible if they agree with at least one accepted
neighbor on both:

- tumor centroid offset **relative to the head centroid** ≤ `--max-rel-dist` (default 40 mm), and
- tumor **volume ratio** ≤ `--vol-ratio` (default 10×)

same greedy-removal scheme as Pipeline A, but robust to scanner/pose differences
across visits.

### Running

Full study from the repo root:

```bash
python scripts_nnu/06_nnu_run_iterative.py --rounds 4 --gpus 3
```

Defaults: baseline 250 epochs @ 1e-2; warm-started rounds 75 epochs @ 1e-3.
Pseudo-label gates match Pipeline A (`--tau-pos 0.95`, `--min-cc-frac 0.2`,
`--neg-frac 0.9`, volume p2–p98). Optional flags: `--no-tta`,
`--no-neg-pseudo`, and the gate overrides. Outputs to `results/round{k}_test_nnu.json`,
`results/iterative_table_nnu.jsonl`, `results/iterative_dice_nnu.png`; per-round
checkpoints snapshotted to `runs_nnu/round{k}/best_nnu.pth`.

Individual stages:

```bash
python scripts_nnu/01_nnu_prepare_dataset.py --rows <rows.jsonl>
python scripts_nnu/02_nnu_plan_preprocess.py --val data/manifests/split_val.jsonl
python scripts_nnu/03_nnu_train.py --gpus 3 --epochs 250 --lr 1e-2
python scripts_nnu/04_nnu_pseudo_label.py --pool data/manifests/unlabeled_pool.jsonl \
    --out data/pseudo_nnu/round1 --gpus 3
python scripts_nnu/05_nnu_eval_test.py --rows data/manifests/split_test.jsonl \
    --out results/round0_test_nnu.json --gpus 3
```

### Verified

End-to-end smoke-tested on a scratch 4-case dataset: dataset build → planning →
`splits_final.json` → training 1 epoch → warm-start → 2-way sharded prediction →
selection gates → test eval. The consistency filter's keep / reject /
single-timepoint paths are unit-tested with synthetic volumes.