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:

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

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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:

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

Individual stages are standalone, e.g.:

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.

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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:

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:

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.