feat: refactor path handling and add configuration for external data directories

.gitignore

@@ -1,4 +1,4 @@
-data/
+data
 runs/
 results/
 logs/

.vscode/settings.json

@@ -0,0 +1,4 @@
+{
+    "python-envs.defaultEnvManager": "ms-python.python:conda",
+    "python-envs.defaultPackageManager": "ms-python.python:conda"
+}

AGENTS.md

@@ -16,6 +16,8 @@ Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), num
 
 Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.
 
+**All path config lives in `config/paths.json`** — the external data roots the code reads or writes (`data`, `lee`, `m6`, `ntuh_register_inv`). Code resolves them through `src.common` (`PATHS` dict, `path(name)` lookup, `DATA`/`d()` for `data/...` paths). To move any dataset, only edit `config/paths.json`; the repo root is derived from the code location (override with `LONGITUDINAL_ROOT` if needed).
+
 ## Conventions
 
 - All Python commands must run inside the `longitudinal` conda environment.

config/paths.json

@@ -0,0 +1,6 @@
+{
+  "data": "/mnt/t24/Public/xfr/longitudinal",
+  "lee": "/mnt/t24/Public/lee",
+  "m6": "/mnt/pve/WORKSPACE/M6-2025/nii",
+  "ntuh_register_inv": "/mnt/pve/SRS/NTUH2022G4/register_inv"
+}

scripts/01_build_ntuh_manifest.py

@@ -10,7 +10,7 @@ import sys
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 import re
 from collections import defaultdict
-from src.common import ROOT, save_jsonl
+from src.common import ROOT, DATA, save_jsonl, path
 
 T1C_RE = re.compile(r"T1.*\+C|\+C.*T1")
 EXCL_RE = re.compile(r"MRA|FLAIR|TOF|T2|SWI|DWI|_ROI|ROI1", re.I)
@@ -18,8 +18,8 @@ SPINE_RE = re.compile(r"[TLC]\s?\d+\s?[-–]\s?[TLC]\s?\d+|[TLC]\d{2}", re.I)
 
 
 def main(limit=None):
-    out = os.path.join(ROOT, "data", "manifests", "ntuh.jsonl")
+    out = os.path.join(DATA, "manifests", "ntuh.jsonl")
-    reg_inv = "/mnt/pve/SRS/NTUH2022G4/register_inv"
+    reg_inv = path("ntuh_register_inv")
     cands = {}  # (subj, case, ts) -> list of (pref, ser, fname)
     n_subj = 0
     for s in sorted(os.listdir(reg_inv)):

scripts/02_build_m6_dataset.py

@@ -16,9 +16,9 @@ import argparse
 import numpy as np
 import SimpleITK as sitk
 from concurrent.futures import ProcessPoolExecutor, as_completed
-from src.common import ROOT, d, save_jsonl, read_nii_img
+from src.common import ROOT, DATA, d, save_jsonl, read_nii_img, path
 
-BASE = "/mnt/pve/WORKSPACE/M6-2025/nii"
+BASE = path("m6")
 T1C_RE = re.compile(r"T1.*\+C|fl3d.*\+.*c", re.I)
 EXCL_RE = re.compile(r"FLAIR|DTI|vibe|dixon|t2|SWI|T2|MPR_Cor", re.I)
 
@@ -138,8 +138,8 @@ def main():
     unlabeled = [r for r in unlabeled if r["key"] not in {k["key"] for k in keep}]
     for r in keep + unlabeled:
         r.pop("ct", None)
-    save_jsonl(keep, os.path.join(ROOT, "data/manifests/m6_labeled.jsonl"))
+    save_jsonl(keep, os.path.join(DATA, "manifests/m6_labeled.jsonl"))
-    save_jsonl(unlabeled, os.path.join(ROOT, "data/manifests/m6_unlabeled.jsonl"))
+    save_jsonl(unlabeled, os.path.join(DATA, "manifests/m6_unlabeled.jsonl"))
     print(f"final labeled={len(keep)} unlabeled={len(unlabeled)}")
 
 

scripts/03_scan_lee_t1c.py

@@ -15,9 +15,9 @@ import re
 import json
 import argparse
 import random
-from src.common import ROOT, save_jsonl, load_jsonl, is_head_series
+from src.common import ROOT, DATA, save_jsonl, load_jsonl, is_head_series, path
 
-BASE = "/mnt/t24/Public/lee"
+BASE = path("lee")
 T1_NAME_RE = re.compile(r"t1|tfl|spgr|mp2rage|tse3d|vfl|mpage", re.I)
 EXCL_RE = re.compile(r"\bt2\b|dwi|dti|mra|mrv|angi|swi|bold|\bpp2d|\bpp3d|perf|t2\*|t2star", re.I)
 
@@ -128,15 +128,17 @@ def scan_timepoint(sid, date, tpd):
                      "jpg_dir": jpg_dir,
                      "max_sp": max_spacing(t),
                      "acq": "3" if "3D" in t.get((24, 35), "") else "2"})
-    # series thicker than MAX_SPACING are kept only when this timepoint has
+    # series thicker than MAX_SPACING are the primary candidates only when this
-    # no thinner valid T1c candidate; then keep the best thick one
+    # timepoint has no thinner valid T1c; the others are flagged
+    # thick_dropped: out of the selected manifest, but kept in raw so 04 can
+    # fall back to them when every other T1c of the timepoint is lost
     thin = [c for c in cand if c["max_sp"] <= MAX_SPACING]
-    if thin:
+    keep = thin if thin else [max(cand, key=lambda c: (c["acq"], c["n_slices"]))]
-        cand = thin
+    dropped = [c for c in cand if c not in keep]
-    elif cand:
+    for c in dropped:
-        cand = [max(cand, key=lambda c: (c["acq"], c["n_slices"]))]
+        c["thick_dropped"] = True
-    cand.sort(key=lambda c: (c["acq"], c["n_slices"]), reverse=True)
+    keep.sort(key=lambda c: (c["acq"], c["n_slices"]), reverse=True)
-    return cand
+    return keep + dropped
 
 
 def main():
@@ -151,7 +153,7 @@ def main():
                     help="select every head T1c candidate (no subject/timepoint caps)")
     args = ap.parse_args()
     random.seed(args.seed)
-    raw_path = os.path.join(ROOT, "data/manifests/lee_t1c_raw.jsonl")
+    raw_path = os.path.join(DATA, "manifests/lee_t1c_raw.jsonl")
     if args.select_only:
         rows = load_jsonl(raw_path)
         print(f"select-only: {len(rows)} raw rows")
@@ -162,6 +164,7 @@ def main():
         with ThreadPoolExecutor(max_workers=32) as ex:
             head = list(ex.map(is_head_row, rows))
         rows = [r for r, ok in zip(rows, head) if ok]
+        rows = [r for r in rows if not r.get("thick_dropped")]
         print(f"head/brain rows: {len(rows)}")
     else:
         rows = []
@@ -179,22 +182,28 @@ def main():
                     continue
                 date = tp[:8]
                 for c in scan_timepoint(sid, date, tpd):
-                    rows.append({"sid": sid, "date": date, "tp": tp,
+                    row = {"sid": sid, "date": date, "tp": tp,
                            "jpg_dir": c["jpg_dir"], "ser": c["ser"], "n_slices": c["n_slices"],
                            "txt_first": c["txt_first"], "why": c["why"],
-                                 "key": f"lee_{sid}_{date}_s{c['ser']}"})
+                           "key": f"lee_{sid}_{date}_s{c['ser']}"}
+                    if c.get("thick_dropped"):
+                        row["thick_dropped"] = True
+                    rows.append(row)
             if i % 100 == 0 and i:
                 print(f"scanned {i}/{len(sids)} subjects, {len(rows)} T1c candidates", flush=True)
         save_jsonl(rows, raw_path)
         print(f"total T1c candidates: {len(rows)}")
 
+    # thick_dropped rows stay out of the selection; they remain in raw for 04's
+    # last-resort pass on timepoints that otherwise yield no T1c volume
+    base = [r for r in rows if not r.get("thick_dropped")]
     if args.all:
         # no caps: every head T1c candidate enters the dataset
-        sel = list(rows)
+        sel = list(base)
     else:
         # subset selection: prefer subjects with more timepoints (longitudinal consistency)
         by_subj = {}
-        for r in rows:
+        for r in base:
             by_subj.setdefault(r["sid"], []).append(r)
         for v in by_subj.values():
             v.sort(key=lambda x: x["date"])
@@ -219,7 +228,7 @@ def main():
         for k in list(single)[: args.max_single_subj]:
             sel.extend(single[k])
     sel.sort(key=lambda x: (x["sid"], x["date"], int(x["ser"])))
-    save_jsonl(sel, os.path.join(ROOT, "data/manifests/lee_t1c_selected.jsonl"))
+    save_jsonl(sel, os.path.join(DATA, "manifests/lee_t1c_selected.jsonl"))
     print(f"selected: {len(sel)} timepoints from {len(set(r['sid'] for r in sel))} subjects")
 
 

scripts/04_reconstruct_lee.py

@@ -5,7 +5,7 @@ slice positions are reconstructed by fitting a linear IPP(s) model, validated
 against all available samples.
 Usage: python scripts/04_reconstruct_lee.py [--manifest ...selected.jsonl] [--workers 48]
 Output: data/lee_nii/<sid>/<date>_s<ser>.nii.gz (uint8, native grid)
-        + data/screenshots/<sid>/<date>_s<ser>.png QA screenshot (1x3 axial/coronal/sagittal)
+        + data/qa/<sid>/<date>_s<ser>.png QA screenshot (1x3 axial/coronal/sagittal)
 Non-head (spine/abdomen/breast/...) series are rejected via is_head_series.
 Fallback: if a timepoint otherwise yields no series, its rows are retried with
 a relaxed min head extent (--fallback-extent, default 40 mm, vs 60 mm), so a
@@ -14,6 +14,12 @@ candidate for that exam.
 Dynamic-frame series (descriptions like "(exam/frame/phase)-(exam/frame/phase)",
 contrast-dynamics exports) are excluded whenever the same timepoint has other
 T1c candidates, and any artifacts of theirs are pruned.
+Last resort: a timepoint whose T1c series all fail is reconstructed from ALL
+of its T1c candidates, including thick series the scan filtered out of the
+selected manifest (thick_dropped rows of the raw manifest).
+Last ditch: a timepoint that still has no T1c volume keeps its candidates
+with no min-extent floor (all other quality gates still apply) — a thin slab
+is preferred over an empty timepoint.
 """
 import os
 import sys
@@ -26,7 +32,7 @@ import numpy as np
 import SimpleITK as sitk
 from concurrent.futures import ProcessPoolExecutor, as_completed
 from PIL import Image
-from src.common import ROOT, d, load_jsonl, is_head_series
+from src.common import ROOT, DATA, d, load_jsonl, is_head_series
 from make_screenshots import screenshot_from_volume
 
 
@@ -94,10 +100,10 @@ def prune_artifacts(keys):
     for k in sorted(keys):
         parts = k.split("_")
         sid, date, sser = parts[1], parts[2], parts[3]
-        for p in (os.path.join(ROOT, "data", "lee_nii", sid, f"{date}_{sser}.nii.gz"),
+        for p in (os.path.join(DATA, "lee_nii", sid, f"{date}_{sser}.nii.gz"),
-                  os.path.join(ROOT, "data", "proc", k + ".nii.gz"),
+                  os.path.join(DATA, "proc", k + ".nii.gz"),
-                  os.path.join(ROOT, "data", "procmeta", k + ".json"),
+                  os.path.join(DATA, "procmeta", k + ".json"),
-                  os.path.join(ROOT, "data", "screenshots", sid, f"{date}_{sser}.png")):
+                  os.path.join(DATA, "qa", sid, f"{date}_{sser}.png")):
             if os.path.exists(p):
                 os.remove(p)
                 n += 1
@@ -236,7 +242,7 @@ def reconstruct(row, extent_min=60.0):
     img.SetDirection(direction)
     os.makedirs(os.path.dirname(out), exist_ok=True)
     sitk.WriteImage(img, out, True)
-    shot = screenshot_from_volume(vol, direction, row["key"], d("data/screenshots"),
+    shot = screenshot_from_volume(vol, direction, row["key"], d("data/qa"),
                               spacing=(float(ps[1]), float(ps[0]), float(dmed)),
                               series_desc=series, protocol=proto)
     msg = f"{vol.shape} n={nread}" + (f" shot={os.path.basename(shot)}" if shot
@@ -244,7 +250,7 @@ def reconstruct(row, extent_min=60.0):
     return row["key"], True, msg
 
 
-def run_pool(rows, extent_min, workers):
+def run_pool(rows, extent_min, workers, reasons=None):
     ok = err = 0
     if rows:
         with ProcessPoolExecutor(max_workers=workers) as ex:
@@ -258,6 +264,8 @@ def run_pool(rows, extent_min, workers):
                     ok += 1
                 else:
                     err += 1
+                    if reasons is not None:
+                        reasons[k] = msg
                     if err <= 40:
                         print(" ERR", k, msg, flush=True)
                 if i % 100 == 0:
@@ -278,37 +286,120 @@ def zero_tp_rows(uniq):
             and r["sid"] in sub_ok and (r["sid"], r["date"]) not in tp_ok]
 
 
+def write_excluded_notes(zero_rows, all_rows, thick_rows, dyn_excl, reasons):
+    """Write data/qa/<sid>/<date>_no_t1c.md for every timepoint with no
+    surviving T1c volume, listing each candidate series and its rejection reason."""
+    tps = {(r["sid"], r["date"]) for r in zero_rows}
+    written = []
+    for sid, date in sorted(tps):
+        lst = [r for r in all_rows if (r["sid"], r["date"]) == (sid, date)]
+        lst += [r for r in thick_rows if (r["sid"], r["date"]) == (sid, date)]
+        lst.sort(key=lambda r: int(r["ser"]))
+        lines = [f"# {sid} {date}: no T1c volume", "",
+                 "This timepoint has no reconstructed T1c volume: every T1c "
+                 "candidate series was rejected.", "",
+                 "| series | slices | description | reason |",
+                 "|---|---|---|---|"]
+        for r in lst:
+            if r["key"] in dyn_excl:
+                why = "excluded: dynamic-frame series (other T1c in this timepoint)"
+            else:
+                why = reasons.get(r["key"], "no output")
+            desc = (r.get("why") or "").strip().replace("|", "/")
+            lines.append(f"| s{r['ser']} | {r.get('n_slices', '?')} | {desc} | {why} |")
+        out = os.path.join(DATA, "qa", sid, f"{date}_no_t1c.md")
+        os.makedirs(os.path.dirname(out), exist_ok=True)
+        with open(out, "w") as f:
+            f.write("\n".join(lines) + "\n")
+        written.append(out)
+    return written
+
+
+def remove_stale_notes():
+    """Drop no_t1c notes for timepoints that now have at least one volume."""
+    import glob
+    n = 0
+    for f in glob.glob(os.path.join(DATA, "qa", "*", "*_no_t1c.md")):
+        sid = os.path.basename(os.path.dirname(f))
+        date = os.path.basename(f)[:8]
+        if glob.glob(os.path.join(DATA, "lee_nii", sid, f"{date}_s*.nii.gz")):
+            os.remove(f)
+            n += 1
+    return n
+
+
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--manifest", default=os.path.join(ROOT, "data/manifests/lee_t1c_selected.jsonl"))
+    ap.add_argument("--manifest", default=os.path.join(DATA, "manifests/lee_t1c_selected.jsonl"))
     ap.add_argument("--workers", type=int, default=48)
     ap.add_argument("--fallback-extent", type=float, default=40.0,
                     help="min head extent (mm) when retrying timepoints that otherwise "
                          "yielded no series (default 40 vs the usual 60; 0 disables)")
     ap.add_argument("--fallback-only", action="store_true",
                     help="skip the main pass; only run the zero-timepoint fallback")
+    ap.add_argument("--raw-manifest",
+                    default=os.path.join(DATA, "manifests/lee_t1c_raw.jsonl"),
+                    help="raw candidate manifest holding thick_dropped rows")
     args = ap.parse_args()
     rows = load_jsonl(args.manifest)
-    seen, uniq = set(), []
+    seen, uniq_all = set(), []
     for r in rows:  # same key can appear twice (double-exported timepoints)
         if r["key"] not in seen:
             seen.add(r["key"])
-            uniq.append(r)
+            uniq_all.append(r)
-    dyn_excl = excluded_dynamic_keys(uniq)
+    dyn_excl = excluded_dynamic_keys(uniq_all)
+    uniq = uniq_all
     if dyn_excl:
         n = prune_artifacts(dyn_excl)
-        uniq = [r for r in uniq if r["key"] not in dyn_excl]
+        uniq = [r for r in uniq_all if r["key"] not in dyn_excl]
         print(f"excluded {len(dyn_excl)} dynamic-frame series (other T1c in the same "
               f"timepoint), pruned {n} artifacts")
+    reasons = {}
     if not args.fallback_only:
         todo = [r for r in uniq if not os.path.exists(out_path_for(r))]
         print(f"todo={len(todo)} workers={args.workers}")
-        run_pool(todo, extent_min=60.0, workers=args.workers)
+        run_pool(todo, extent_min=60.0, workers=args.workers, reasons=reasons)
     if args.fallback_extent > 0:
         fb = zero_tp_rows(uniq)
         print(f"fallback todo={len(fb)} extent>={args.fallback_extent:g}mm "
               f"(timepoints with no other T1c candidate)")
-        run_pool(fb, extent_min=args.fallback_extent, workers=args.workers)
+        run_pool(fb, extent_min=args.fallback_extent, workers=args.workers, reasons=reasons)
+    runiq = []
+    if args.fallback_extent > 0 and os.path.exists(args.raw_manifest):
+        # last resort: a timepoint whose T1c series all failed keeps them ALL
+        # (e.g. thick series the scan filtered out of the selected manifest)
+        rseen = set()
+        for r in load_jsonl(args.raw_manifest):
+            if r.get("thick_dropped") and r["key"] not in rseen:
+                rseen.add(r["key"])
+                runiq.append(r)
+        if runiq:
+            zero = {(r["sid"], r["date"]) for r in zero_tp_rows(uniq)}
+            lr = [r for r in runiq
+                  if (r["sid"], r["date"]) in zero and not os.path.exists(out_path_for(r))]
+            print(f"last-resort todo={len(lr)} extent>={args.fallback_extent:g}mm "
+                  f"(keep all T1c of an otherwise-empty timepoint)")
+            run_pool(lr, extent_min=args.fallback_extent, workers=args.workers,
+                     reasons=reasons)
+    # last ditch: a timepoint that still has no T1c volume keeps its
+    # candidates without the min-extent floor (thin slab > empty timepoint)
+    zero = zero_tp_rows(uniq + runiq)
+    if zero:
+        zt = {(r["sid"], r["date"]) for r in zero}
+        ld = [r for r in uniq + runiq
+              if (r["sid"], r["date"]) in zt and not os.path.exists(out_path_for(r))]
+        print(f"last-ditch todo={len(ld)} (empty timepoints keep all T1c, "
+              f"no extent floor)")
+        run_pool(ld, extent_min=0.0, workers=args.workers, reasons=reasons)
+    zero = zero_tp_rows(uniq + runiq)  # thick rescues count as timepoint output
+    if zero:
+        notes = write_excluded_notes(zero, uniq_all, runiq, dyn_excl, reasons)
+        print(f"exclusion notes: {len(notes)}")
+        for p in notes:
+            print("  ", os.path.relpath(p, ROOT))
+    nstale = remove_stale_notes()
+    if nstale:
+        print(f"removed {nstale} stale notes")
 
 
 if __name__ == "__main__":

scripts/05_build_splits.py

@@ -19,7 +19,7 @@ import json
 import argparse
 import random
 import numpy as np
-from src.common import ROOT, d, load_jsonl, save_jsonl, read_nii_arr
+from src.common import ROOT, DATA, d, load_jsonl, save_jsonl, read_nii_arr
 
 
 def proc_row(r, prefix):
@@ -45,7 +45,7 @@ def main():
 
     labeled_rows = []
     for name, prefix in (("ntuh", "ntuh"), ("m6_labeled", "m6")):
-        for r in load_jsonl(os.path.join(ROOT, "data", "manifests", name + ".jsonl")):
+        for r in load_jsonl(os.path.join(DATA, "manifests", name + ".jsonl")):
             if not r.get("label"):
                 continue
             p = proc_row(r, prefix)
@@ -72,14 +72,14 @@ def main():
         else:
             splits["train"].append(r)
     for k, v in splits.items():
-        save_jsonl(v, os.path.join(ROOT, "data/manifests", f"split_{k}.jsonl"))
+        save_jsonl(v, os.path.join(DATA, "manifests", f"split_{k}.jsonl"))
         print(f"  split_{k}: {len(v)} volumes / {len(set(r['subject'] for r in v))} subjects")
 
     labeled_subjects = train_subj | val_subj | test_subj
 
     # unlabeled pool
     pool = []
-    for r in load_jsonl(os.path.join(ROOT, "data/manifests/m6_unlabeled.jsonl")):
+    for r in load_jsonl(os.path.join(DATA, "manifests/m6_unlabeled.jsonl")):
         if f"m6_{r['subject']}" in labeled_subjects:
             continue
         p = proc_row(r, "m6")
@@ -87,10 +87,21 @@ def main():
             p["date"] = r.get("date")
             p["source"] = "m6"
             pool.append(p)
-    lee_sel_path = os.path.join(ROOT, "data/manifests/lee_t1c_selected.jsonl")
+    lee_sel_path = os.path.join(DATA, "manifests/lee_t1c_selected.jsonl")
     lee_rows = {}
+    lee_manifest = []
     if os.path.exists(lee_sel_path):
-        for r in load_jsonl(lee_sel_path):
+        lee_manifest = load_jsonl(lee_sel_path)
+        raw_path = os.path.join(DATA, "manifests", "lee_t1c_raw.jsonl")
+        if os.path.exists(raw_path):
+            # last-resort rescues: thick series the scan filtered out, kept
+            # when their timepoint otherwise yielded no T1c volume
+            have = {r["key"] for r in lee_manifest}
+            for r in load_jsonl(raw_path):
+                if r.get("thick_dropped") and r["key"] not in have:
+                    lee_manifest.append(r)
+                    have.add(r["key"])
+        for r in lee_manifest:
             nii = os.path.join(d("data/lee_nii"), r["sid"], f"{r['date']}_s{r['ser']}.nii.gz")
             proc = os.path.join(d("data/proc"), r["key"] + ".nii.gz")
             if os.path.exists(proc):
@@ -99,7 +110,7 @@ def main():
                 lee_rows[r["key"]] = p
     pool.extend(lee_rows.values())
     pool.sort(key=lambda r: (r["subject"], r.get("date", "")))
-    save_jsonl(pool, os.path.join(ROOT, "data/manifests/unlabeled_pool.jsonl"))
+    save_jsonl(pool, os.path.join(DATA, "manifests/unlabeled_pool.jsonl"))
     print(f"unlabeled pool: {len(pool)} volumes ({sum(1 for r in pool if r['source']=='m6')} m6, "
           f"{sum(1 for r in pool if r['source']=='lee')} lee) from {len(set(r['subject'] for r in pool))} subjects")
 
@@ -118,8 +129,8 @@ def main():
              "p50": float(np.percentile(v, 50)), "p95": float(np.percentile(v, 95)),
              "p98": float(np.percentile(v, 98)), "max": float(v.max()),
              "zero_frac": float((v == 0).mean())}
-    save_jsonl(vols, os.path.join(ROOT, "data/vols.jsonl"))
+    save_jsonl(vols, os.path.join(DATA, "vols.jsonl"))
-    with open(os.path.join(ROOT, "data/vols.json"), "w") as f:
+    with open(os.path.join(DATA, "vols.json"), "w") as f:
         json.dump(stats, f, indent=1)
     print("tumor volumes (mm3):", stats)
 

scripts/06_pseudo_label.py

@@ -17,7 +17,7 @@ import argparse
 import numpy as np
 import SimpleITK as sitk
 from scipy import ndimage
-from src.common import ROOT, d, load_jsonl, save_jsonl, read_nii_arr
+from src.common import ROOT, DATA, d, load_jsonl, save_jsonl, read_nii_arr
 from src import training
 
 
@@ -90,8 +90,8 @@ def main():
     shard = rows[rank::world]
 
     vstats = {}
-    if os.path.exists(os.path.join(ROOT, "data/vols.json")):
+    if os.path.exists(os.path.join(DATA, "vols.json")):
-        vstats = json.load(open(os.path.join(ROOT, "data/vols.json")))
+        vstats = json.load(open(os.path.join(DATA, "vols.json")))
     vol_lo = vstats.get(f"p{args.vol_qp[0]:.0f}", 1.0)
     vol_hi = vstats.get(f"p{args.vol_qp[1]:.0f}", 50000.0)
     if rank == 0:

scripts/09_run_iterative.py

@@ -15,7 +15,7 @@ import argparse
 import json
 import shutil
 import subprocess
-from src.common import ROOT, load_jsonl, save_jsonl
+from src.common import ROOT, DATA, load_jsonl, save_jsonl
 
 
 def run(cmd, log, retries=2):
@@ -51,7 +51,7 @@ def main():
     ap.add_argument("--batch", type=int, default=3)
     args = ap.parse_args()
 
-    man = os.path.join(ROOT, "data/manifests")
+    man = os.path.join(DATA, "manifests")
     train_f = os.path.join(man, "split_train.jsonl")
     val_f = os.path.join(man, "split_val.jsonl")
     test_f = os.path.join(man, "split_test.jsonl")
@@ -69,7 +69,7 @@ def main():
     run(f"python scripts/08_eval.py --rows {test_f} --ckpt runs/round0/best.pt", f"{main_log}")
 
     for k in range(1, args.rounds + 1):
-        pdir = os.path.join(ROOT, "data/pseudo/round" + str(k))
+        pdir = os.path.join(DATA, "pseudo/round" + str(k))
         run(torchrun(args.gpus, "scripts/06_pseudo_label.py",
                      f"--ckpt runs/round{k-1}/best.pt --unlabeled {pool_f} --out {pdir}"), f"{main_log}")
         # build round-k training manifest: labeled + accepted pseudo rows (weighted)
@@ -100,7 +100,7 @@ def main():
             r = json.load(open(f))
             table.append({"round": k, "test_dice": round(r["dice"], 4), "n_test": r["n"],
                           "ckpt_epoch": r.get("epoch")})
-            pf = os.path.join(ROOT, f"data/pseudo/round{k}/summary.json") if k > 0 else None
+            pf = os.path.join(DATA, f"pseudo/round{k}/summary.json") if k > 0 else None
             if pf and os.path.exists(pf):
                 s = json.load(open(pf))
                 table[-1].update({"n_pos": s["n_pos"], "n_neg": s["n_neg"],

scripts/benchmark_pipelines.py

@@ -52,7 +52,7 @@ sys.path.insert(0, os.path.join(ROOT, "scripts_nnu"))
 import numpy as np
 import torch
 
-from src.common import load_jsonl, read_nii_arr
+from src.common import DATA, load_jsonl, read_nii_arr
 from src.training import dice_np
 
 
@@ -95,7 +95,7 @@ def parse_args():
 
 def native_manifests():
     """key -> {img, label} from the labeled source manifests (ntuh + m6_labeled)."""
-    man = os.path.join(ROOT, "data", "manifests")
+    man = os.path.join(DATA, "manifests")
     out = {}
     for name in ("ntuh.jsonl", "m6_labeled.jsonl"):
         p = os.path.join(man, name)
@@ -119,7 +119,7 @@ def native_rows_for(keys):
 
 
 def select_rows(args):
-    man = os.path.join(ROOT, "data", "manifests")
+    man = os.path.join(DATA, "manifests")
     train = [r for r in load_jsonl(os.path.join(man, "split_train.jsonl")) if r.get("plabel")]
     test = [r for r in load_jsonl(os.path.join(man, "split_test.jsonl")) if r.get("plabel")]
     if not train:

scripts/make_screenshots.py

@@ -13,7 +13,7 @@ centered vertically, widths follow the physical aspect of each cut).
 Usage:
   python scripts/make_screenshots.py --keys k1 k2 ...
   python scripts/make_screenshots.py --sample 20 [--keys ...]
-Output: data/screenshots/<patient>/<rest>.png (per-patient, mirrors data/lee_nii)
+Output: data/qa/<patient>/<rest>.png (per-patient, mirrors data/lee_nii)
 """
 import os
 import sys
@@ -163,7 +163,7 @@ def main():
     ap.add_argument("--keys", nargs="*", default=[])
     ap.add_argument("--sample", type=int, default=0)
     ap.add_argument("--seed", type=int, default=0)
-    ap.add_argument("--out", default=d("data/screenshots"))
+    ap.add_argument("--out", default=d("data/qa"))
     args = ap.parse_args()
     os.makedirs(args.out, exist_ok=True)
     allkeys = [os.path.basename(f)[:-7] for f in glob.glob(os.path.join(d("data/proc"), "*.nii.gz"))

scripts/scan_procs.py

@@ -4,6 +4,7 @@ import sys
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 import glob
 import json
+from src.common import DATA
 import numpy as np
 import SimpleITK as sitk
 from concurrent.futures import ProcessPoolExecutor, as_completed
@@ -18,7 +19,7 @@ def check(f):
 
 
 def main():
-    files = sorted(glob.glob(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data", "proc", "*.nii.gz")))
+    files = sorted(glob.glob(os.path.join(DATA, "proc", "*.nii.gz")))
     print("checking", len(files), flush=True)
     bad = []
     with ProcessPoolExecutor(max_workers=48) as ex:
@@ -32,7 +33,7 @@ def main():
     print("BAD volumes:", len(bad))
     for b in sorted(bad):
         print(" ", b)
-    with open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "data", "bad_procs.json"), "w") as f:
+    with open(os.path.join(DATA, "bad_procs.json"), "w") as f:
         json.dump(sorted(bad), f, indent=1)
 
 

scripts/test_dataloader.py

@@ -6,8 +6,8 @@ sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 
 def main():
     from src import training
-    from src.common import load_jsonl
+    from src.common import DATA, load_jsonl
-    rows = load_jsonl(os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "data", "manifests", "split_train.jsonl"))
+    rows = load_jsonl(os.path.join(DATA, "manifests", "split_train.jsonl"))
     for seed in (0, 1, 2):
         ds, dl = training.make_dataloader(rows, (96, 96, 96), 3, True, num_workers=4, seed=seed)
         n_none, n = 0, 0

scripts_monai/monai_common.py

@@ -20,8 +20,9 @@ import os
 import sys
 
 # Data/run dirs: overridable for scratch runs. Code (src/, scripts_nnu/): next to this file.
-ROOT = os.environ.get("LONGITUDINAL_ROOT", "/mnt/b4/xfr/git26/longitudinal")
+ROOT = os.environ.get(
-_REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    "LONGITUDINAL_ROOT", os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+_REPO = ROOT
 sys.path.insert(0, _REPO)
 sys.path.insert(0, os.path.join(_REPO, "scripts_nnu"))
 

scripts_nnu/06_nnu_run_iterative.py

@@ -19,7 +19,7 @@ import subprocess
 sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
 sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 from src.common import load_jsonl, save_jsonl
-from nnu_common import ROOT, d, best_ckpt
+from nnu_common import ROOT, DATA, d, best_ckpt
 
 
 def main():
@@ -41,7 +41,7 @@ def main():
     ap.add_argument("--vol-ratio", type=float, default=10.0)
     args = ap.parse_args()
 
-    man = os.path.join(ROOT, "data/manifests")
+    man = os.path.join(DATA, "manifests")
     train_f = os.path.join(man, "split_train.jsonl")
     val_f = os.path.join(man, "split_val.jsonl")
     test_f = os.path.join(man, "split_test.jsonl")

scripts_nnu/nnu_common.py

@@ -10,9 +10,10 @@ import json
 import shutil
 import subprocess
 
-ROOT = os.environ.get("LONGITUDINAL_ROOT", "/mnt/b4/xfr/git26/longitudinal")
+ROOT = os.environ.get(
+    "LONGITUDINAL_ROOT", os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 sys.path.insert(0, ROOT)
-from src.common import load_jsonl, save_jsonl, read_nii_arr, head_mask_from_image, largest_cc  # noqa: E402
+from src.common import DATA, d, load_jsonl, save_jsonl, read_nii_arr, head_mask_from_image, largest_cc  # noqa: E402
 
 import numpy as np
 import SimpleITK as sitk
@@ -61,12 +62,6 @@ def final_ckpt():
     return os.path.join(fold_dir(), "checkpoint_final.pth")
 
 
-def d(name):
-    p = os.path.join(ROOT, name)
-    os.makedirs(p, exist_ok=True)
-    return p
-
-
 def nnu_env(epoch=None, lr=None, warmstart=None):
     env = dict(os.environ)
     env["nnUNet_raw"] = os.path.join(nnu_root(), "raw")
@@ -366,5 +361,5 @@ def consistency_filter(rows, out_dir, max_rel_dist_mm=40.0, vol_ratio_max=10.0,
 
 
 def load_voxel_stats():
-    p = os.path.join(ROOT, "data/vols.json")
+    p = os.path.join(DATA, "vols.json")
     return json.load(open(p)) if os.path.exists(p) else {}

src/common.py

@@ -6,7 +6,38 @@ import numpy as np
 import SimpleITK as sitk
 from scipy import ndimage
 
-ROOT = os.environ.get("LONGITUDINAL_ROOT", "/mnt/b4/xfr/git26/longitudinal")
+# Repo root: next to this file's parent; overridable for scratch checkouts.
+ROOT = os.environ.get(
+    "LONGITUDINAL_ROOT", os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+
+def _load_paths():
+    """All path config from config/paths.json ({} when absent/unreadable)."""
+    p = os.path.join(ROOT, "config", "paths.json")
+    if os.path.exists(p):
+        try:
+            with open(p) as f:
+                v = json.load(f)
+            if isinstance(v, dict):
+                return v
+        except (OSError, ValueError):
+            pass
+    return {}
+
+
+PATHS = _load_paths()
+
+
+def path(name):
+    """Named external path from config/paths.json (e.g. 'data', 'lee', 'm6')."""
+    try:
+        return PATHS[name]
+    except KeyError:
+        raise KeyError(
+            f"missing path {name!r} in config/paths.json (have: {sorted(PATHS)})")
+
+
+DATA = PATHS.get("data") or os.path.join(ROOT, "data")
 
 # series from non-head (non-brain/non-skull) exams must not enter the dataset
 # (includes "head and neck" exams — neck studies are excluded wholesale)
@@ -22,6 +53,11 @@ def is_head_series(study_desc, series_desc):
 
 
 def d(name):
+    if name == "data":
+        p = DATA
+    elif name.startswith("data/"):
+        p = os.path.join(DATA, name[len("data/"):])
+    else:
         p = os.path.join(ROOT, name)
     os.makedirs(p, exist_ok=True)
     return p