#!/usr/bin/env python3 """ Adapter for 19 OCT public datasets → 41-col manifest (matching private/fundus schema). 19 cohorts: 17 A-class (enumerated via existing unified_metadata.csv): public_oct_kermany, public_oct_octid, public_oct_aroi, public_oct_neh_ut_2021, public_oct_areds2, public_oct_glaucoma, public_oct_nyu_poag, public_oct_olives, public_oct_chiu_dme_2015, public_oct_srinivasan_2014, public_oct_sparsity_sdoct_2012, public_oct_oimhs, public_oct_retouch, public_oct_thoct1800, public_oct_octdl, public_oct_amd_sd, public_oct_c8 + 2 enumerated from source layout: public_oct_octa500, public_oct_uestc Output layout (under {output_root}): extracted/{cohort}/{hash[:2]}/{hash}/{bscan.png|bscan_NNN.png}{+meta.json}{+masks} manifest/{cohort}_images.parquet manifest/{cohort}_sidecar.parquet (where applicable) captions/{cohort}_captions.parquet Notes: - Most cohorts use file-level study_id (1 bscan = 1 study). patient_hash is shared across bscans of same patient when patient_id is known. - OLIVES uses study_id = hash(patient_eye_visit), patient_hash = hash(patient). - OCTA500 and UESTC use file-level study_id (volume info preserved in source_basename). - Masks (where present) are copied to study_dir as auxiliary files; not in manifest rows. """ import argparse import os import re from collections import defaultdict from pathlib import Path import pandas as pd from public_common import ( default_base_fields, study_hash_for, rel_file_path, ) from oct_public_common import ( caption_l1_oct, caption_l3_oct, device_phrase, save_mask_preserve, run_cohort, ) # ============================================================ # Disease label → (diagnosis_group, severity) # ============================================================ DR_SEVERITY = {0: "none", 1: "mild", 2: "moderate", 3: "severe", 4: "proliferative"} DISEASE_MAP = { # Normal variants "NORMAL": ([], "none"), "Normal": ([], "none"), "NOR": ([], "none"), "NO": ([], "none"), "Control": ([], "none"), "CONTROL": ([], "none"), # Disease classes "CNV": (["CNV"], "unknown"), "DME": (["DME"], "unknown"), "DRUSEN": (["DRUSEN"], "unknown"), "DR": (["DR"], "unknown"), "AMD": (["AMD"], "unknown"), "wet_AMD": (["wet_AMD"], "unknown"), "nAMD": (["nAMD"], "unknown"), "AMD/RVO": (["AMD", "RVO"], "unknown"), "CSR": (["CSR"], "unknown"), "MH": (["MH"], "unknown"), "MH_Stage1": (["MH"], "mild"), "MH_Stage2": (["MH"], "moderate"), "MH_Stage3": (["MH"], "severe"), "MH_Stage4": (["MH"], "severe"), "Glaucoma": (["glaucoma"], "unknown"), "POAG": (["glaucoma"], "unknown"), "ERM": (["ERM"], "unknown"), "RVO": (["RVO"], "unknown"), "RAO": (["RAO"], "unknown"), "VID": (["VID"], "unknown"), # Unknown / fallback "Unknown": ([], "unknown"), } def map_disease(label): return DISEASE_MAP.get(str(label).strip(), (["unknown_disease"], "unknown")) # ============================================================ # Per-cohort static config (device, anatomy, scan_protocol, ethnicity) # ============================================================ COHORT_CONFIG = { "public_oct_kermany": dict( phrase="Kermany 2018 OCT classification dataset (UCSD / Cell)", vendor="heidelberg", model="spectralis", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="Mixed", hospital_domain="kermany_ucsd_v1"), "public_oct_octid": dict( phrase="OCTID Indian retinal OCT classification dataset", vendor="zeiss", model="cirrus_hd_oct_5000", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="South Asian", hospital_domain="octid_sankara_v1"), "public_oct_aroi": dict( phrase="AROI nAMD layer segmentation dataset (Croatia)", vendor="zeiss", model="cirrus_hd_oct_4000", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="European", hospital_domain="aroi_zagreb_v1"), "public_oct_neh_ut_2021": dict( phrase="NEH-UT-2021 Iranian retinal OCT dataset", vendor="heidelberg", model="spectralis_sd_oct", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="Middle Eastern", hospital_domain="neh_ut_2021_v1"), "public_oct_areds2": dict( phrase="AREDS2 ancillary SD-OCT AMD dataset (NEI)", vendor="bioptigen", model="bioptigen_sd_oct", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="Mixed", hospital_domain="areds2_nei_v1"), "public_oct_glaucoma": dict( phrase="Glaucoma OCT and fundus dataset (TD-OCT)", vendor="zeiss", model="stratus_oct", tech="td_oct", anatomy="optic_disc", scan_protocol="single_shot", ethnicity="unknown", hospital_domain="glaucoma_oct_v1"), "public_oct_nyu_poag": dict( phrase="NYU POAG retinal OCT dataset", vendor="unknown", model="unknown", tech="unknown", anatomy="optic_disc", scan_protocol="volume_3d_macula", ethnicity="unknown", hospital_domain="nyu_poag_v1"), "public_oct_olives": dict( phrase="OLIVES longitudinal DR and DME OCT dataset", vendor="heidelberg", model="spectralis_hra_oct", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="unknown", hospital_domain="olives_v1"), "public_oct_chiu_dme_2015": dict( phrase="Chiu et al. 2015 DME 8-layer segmentation dataset (Duke)", vendor="heidelberg", model="spectralis", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="unknown", hospital_domain="chiu_duke_2015_v1"), "public_oct_srinivasan_2014": dict( phrase="Srinivasan et al. 2014 AMD-DME-Normal OCT dataset (Duke/Harvard/Michigan)", vendor="heidelberg", model="spectralis", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="unknown", hospital_domain="srinivasan_2014_v1"), "public_oct_sparsity_sdoct_2012": dict( phrase="Sparsity SDOCT 2012 AMD vs control dataset", vendor="bioptigen", model="bioptigen_sd_oct", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="unknown", hospital_domain="sparsity_sdoct_2012_v1"), "public_oct_oimhs": dict( phrase="OIMHS macular hole staging and layer segmentation dataset", vendor="heidelberg", model="spectralis_sd_oct", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="Asian", hospital_domain="oimhs_china_v1"), "public_oct_retouch": dict( phrase="RETOUCH 2017 retinal fluid segmentation challenge dataset", vendor="varies", model="varies", tech="sd_oct", # 3 sub-vendors anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="European", hospital_domain="retouch_v1"), "public_oct_thoct1800": dict( phrase="THOCT1800 Tsinghua AMD-DME-Normal OCT dataset", vendor="zeiss", model="cirrus_hd_oct", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="Asian", hospital_domain="thoct1800_tsinghua_v1"), "public_oct_octdl": dict( phrase="OCTDL Russian 7-class retinal OCT dataset", vendor="optovue", model="rtvue_xr_avanti", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="European", hospital_domain="octdl_russia_v1"), "public_oct_amd_sd": dict( phrase="AMD-SD wet AMD multi-class segmentation dataset (China)", vendor="zeiss", model="cirrus_hd_oct_5000", tech="sd_oct", anatomy="macula", scan_protocol="single_shot", ethnicity="Asian", hospital_domain="amd_sd_nanchang_v1"), "public_oct_c8": dict( phrase="C8 compiled 8-class retinal OCT classification dataset (Kaggle)", vendor="unknown", model="unknown", tech="unknown", anatomy="macula", scan_protocol="single_shot", ethnicity="unknown", hospital_domain="c8_kaggle_v1"), "public_oct_octa500": dict( phrase="OCTA-500 multi-modal OCT-A and OCT structural retinal dataset", vendor="optovue", model="rtvue_xr_avanti", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="Asian", hospital_domain="octa500_njust_v1"), "public_oct_uestc": dict( phrase="UESTC despeckling 3D OCT dataset (BMizar + Spectralis)", vendor="varies", model="varies", tech="sd_oct", anatomy="macula", scan_protocol="volume_3d_macula", ethnicity="Asian", hospital_domain="uestc_sichuan_v1"), } # ============================================================ # Shared row+caps builder for "standard" cohorts # ============================================================ def _shared_build_row_caps(meta, cohort, cohort_phrase, has_segmentation_fn=None, l3_extras_fn=None, scan_protocol_override=None): """Default row/caps builder. Returns (list_of_rows, list_of_captions). Multi-slice studies emit one row per slice; all share study_id + patient_hash.""" cfg = COHORT_CONFIG[cohort] sh = meta["study_hash"] ph = meta["patient_hash"] eye = meta.get("eye", "unknown") disease_label = meta.get("disease_label", "Unknown") dx, sev = map_disease(disease_label) base = default_base_fields( cohort, sh, patient_hash=ph, eye=eye, ethnicity=cfg["ethnicity"], hospital_domain=cfg["hospital_domain"]) base["device_vendor"] = meta.get("device_vendor", cfg["vendor"]) base["device_model"] = meta.get("device_model", cfg["model"]) base["diagnosis_group"] = dx base["severity"] = sev if disease_label and disease_label != "Unknown": base["diagnosis_source"] = meta.get("diagnosis_source", "expert_label") rows, caps = [], [] n_slices = meta["n_slices"] for slc in meta["slices"]: idx = slc["idx"] if idx is None: image_id = f"{cohort}_{sh}_bscan" file_path = rel_file_path(cohort, sh, "bscan.png") else: image_id = f"{cohort}_{sh}_bscan_{idx:03d}" file_path = rel_file_path(cohort, sh, slc["fname"]) row = dict(base) has_seg = has_segmentation_fn(meta, slc) if has_segmentation_fn else bool( meta.get("has_segmentation_mask")) row.update({ "image_id": image_id, "file_path": file_path, "file_format": "png", "modality": "oct_bscan", "anatomy": cfg["anatomy"], "device_technology": cfg["tech"], "scan_protocol": scan_protocol_override or cfg["scan_protocol"], "bscan_index": idx, "image_height_px": slc["h"], "image_width_px": slc["w"], "has_segmentation": has_seg, "n_layers_visible": 0, "is_valid": True, }) rows.append(row) # 把 row 中的 device 传给 L1 caption (per-row, RETOUCH/UESTC 多设备 cohort 也对) caps.extend(caption_l1_oct(image_id, cohort_phrase, eye, device_vendor=row["device_vendor"], device_model=row["device_model"])) # L3 也把设备短语 prepend (若已知) dev = device_phrase(row["device_vendor"], row["device_model"]) if dev: l3_parts = [f"An OCT B-scan from {dev}, {cohort_phrase}"] else: l3_parts = [f"An OCT B-scan from the {cohort_phrase}"] if disease_label and disease_label not in ("Unknown",): l3_parts.append(f"label: {disease_label}") if idx is not None and n_slices > 1: l3_parts.append(f"slice {idx+1} of {n_slices}") if l3_extras_fn: l3_parts.extend(l3_extras_fn(meta, slc)) caps.append(caption_l3_oct(image_id, ", ".join(l3_parts) + ".", "manifest_fields+csv_labels")) return rows, caps # ============================================================ # 17 A-class enumerators (read from unified_metadata.csv) # ============================================================ def enum_from_csv_simple(ds_df, in_root, csv_dataset_name): """File-level studies, patient_hash from patient_id when present. study_basename = relative path with separators normalized to ensure uniqueness across train/test/disease subdirs.""" in_root = Path(in_root) items = [] seen_basenames = set() for _, r in ds_df.iterrows(): src = r["image_path"] if not os.path.exists(src): continue # Build unique basename from path relative to in_root try: rel = str(Path(src).relative_to(in_root)) except ValueError: rel = src # Sanitize: replace path separators + spaces + non-ascii safely study_basename = re.sub(r"[^A-Za-z0-9._-]", "_", rel) if study_basename in seen_basenames: # Should not happen given uniqueness of file paths, but defensive continue seen_basenames.add(study_basename) pid = str(r.get("patient_id", "")).strip() patient_basename = f"patient_{pid}" if pid and pid not in ("Unknown", "nan", "") else study_basename items.append({ "study_basename": study_basename, "patient_basename": patient_basename, "study_meta": { "disease_label": r.get("disease_label", "Unknown"), "eye": str(r.get("eye", "unknown")) if str(r.get("eye", "unknown")) != "Unknown" else "unknown", "patient_id": pid if pid and pid != "Unknown" else None, "device_csv": r.get("device", "unknown"), "label_granularity": r.get("label_granularity", "b-scan"), "notes": r.get("notes", ""), }, "slices": [{"src_path": src, "slice_idx": None}], }) return items def enum_olives(ds_df, in_root): """Special: study_id = hash(patient_eye_visit), patient_hash = hash(patient). Multiple bscans of the same (patient, eye, visit) share study_id with slice_idx.""" visit_path_re = re.compile(r"/W(\d+)/") # TREX flat filename: 11-01-001_W100_OD_0.tif → patient=01-001, visit=W100, eye=OD, slc=0 visit_fname_re = re.compile(r"_W(\d+)_(O[DS])_(\d+)\b") groups = defaultdict(list) for _, r in ds_df.iterrows(): src = r["image_path"] if not os.path.exists(src): continue pid = str(r.get("patient_id", "Unknown")) eye = str(r.get("eye", "Unknown")) stem = Path(src).stem slc = None # Prefer path-based extraction (Prime_FULL: XX-YYY/Wn/OD/N.png) m_path = visit_path_re.search(src) if m_path: visit = m_path.group(1) # slice = stem (numeric) if stem.isdigit(): slc = int(stem) else: # TREX flat name m_fn = visit_fname_re.search(stem) if m_fn: visit = m_fn.group(1) if eye == "Unknown": eye = m_fn.group(2) slc = int(m_fn.group(3)) # Patient ID from TREX flat name: 11-XX-YYY_... → pid = XX-YYY if pid in ("Unknown", "nan", ""): pid_m = re.match(r"\d+-(\d{2}-\d{3})_", stem) if pid_m: pid = pid_m.group(1) else: visit = "unknown" groups[(pid, eye, visit)].append((src, slc, r.get("disease_label", "Unknown"), r.get("notes", ""))) items = [] for (pid, eye, visit), files in groups.items(): # Order by slice index files.sort(key=lambda x: (x[1] if x[1] is not None else 0)) slices = [{"src_path": src, "slice_idx": i} for i, (src, _, _, _) in enumerate(files)] # disease_label: take majority (should be uniform within group) diseases = [f[2] for f in files] disease = max(set(diseases), key=diseases.count) items.append({ "study_basename": f"{pid}_{eye}_W{visit}", "patient_basename": f"patient_{pid}", "study_meta": { "disease_label": disease, "eye": eye if eye in ("OD", "OS") else "unknown", "patient_id": pid, "visit": f"W{visit}", "label_granularity": "visit", "notes": files[0][3], }, "slices": slices, }) return items def enum_retouch(ds_df, in_root): """RETOUCH 3 device sub-cohorts encoded in CSV 'notes' field. CSV image_path filenames are flat numerics (1.png, 1000.png, ...) — original volume grouping is LOST in the user's flat enumeration. We thus use file-level studies, but preserve device subset → device_vendor/model in the row.""" items = [] in_root = Path(in_root) for _, r in ds_df.iterrows(): src = r["image_path"] if not os.path.exists(src): continue stem = Path(src).stem notes = str(r.get("notes", "")) dev_m = re.search(r"TrainingSet-(\w+)", notes) dev = dev_m.group(1) if dev_m else "Unknown" dev_model = {"Cirrus": "cirrus_hd_oct", "Spectralis": "spectralis", "Topcon": "topcon_3d_oct"}.get(dev, "unknown") dev_vendor = {"Cirrus": "zeiss", "Spectralis": "heidelberg", "Topcon": "topcon"}.get(dev, "unknown") # Include device in basename to avoid collision across 3 subsets (Spectralis/1.png # and Cirrus/1.png both exist as separate volumes) study_basename = f"retouch_{dev}_{stem}" items.append({ "study_basename": study_basename, "patient_basename": study_basename, # no patient grouping recoverable from CSV "study_meta": { "disease_label": r.get("disease_label", "AMD/RVO"), "eye": "unknown", "patient_id": None, "device_vendor": dev_vendor, "device_model": dev_model, "subset": dev, "label_granularity": "b-scan", # downgraded from volume since we lost grouping "has_segmentation_mask": True, }, "slices": [{"src_path": src, "slice_idx": None}], }) return items def enum_oimhs_with_demographics(ds_df, in_root): """Items + sidecar. One patient has two eyes (two eye_ids in Images/), each with its own copy of files named 1.png, 2.png, etc. → must include eye_id in basename to disambiguate same-patient same-filename collisions.""" items = [] for _, r in ds_df.iterrows(): src = r["image_path"] if not os.path.exists(src): continue stem = Path(src).stem # path is Images//.png → eye_id is parent dir name eye_id = Path(src).parent.name pid = str(r.get("patient_id", "Unknown")) items.append({ "study_basename": f"oimhs_p{pid}_e{eye_id}_{stem}", "patient_basename": f"patient_{pid}", "study_meta": { "disease_label": r.get("disease_label", "MH"), "eye": str(r.get("eye", "unknown")) if str(r.get("eye", "Unknown")) != "Unknown" else "unknown", "patient_id": pid, "eye_id": eye_id, "age": str(r.get("age", "Unknown")), "gender": str(r.get("gender", "Unknown")), "stage": r.get("disease_label", "").replace("MH_Stage", "") if "Stage" in str(r.get("disease_label", "")) else "Unknown", "label_granularity": "eye", }, "slices": [{"src_path": src, "slice_idx": None}], }) return items def enum_octdl_with_demographics(ds_df, in_root): """OCTDL with sex/year(→age)/subcategory/condition sidecar. Note: CSV's image_path lacks the .jpg extension and disease subfolder. We have to reconstruct the real path: {OCTDL_root}/OCTDL/{disease}/{stem}.jpg.""" items = [] # Find OCTDL root by walking disk octdl_root = None for p in Path(in_root / "23" if not isinstance(in_root, Path) else Path(in_root) / "23").rglob("OCTDL"): if p.is_dir() and (p / "AMD").exists(): octdl_root = p break if octdl_root is None: print("[octdl] ERROR: cannot find OCTDL root with AMD subdir") return [] for _, r in ds_df.iterrows(): src_csv = r["image_path"] disease = r.get("disease_label", "AMD") stem = Path(src_csv).name # CSV path's last segment = file stem (no ext) # Try .jpg under disease subdir first for ext in (".jpg", ".jpeg", ".png", ".JPG", ".JPEG"): candidate = octdl_root / disease / f"{stem}{ext}" if candidate.exists(): src = str(candidate) break else: continue # file truly missing pid = str(r.get("patient_id", "Unknown")) items.append({ "study_basename": f"octdl_{disease}_{stem}", "patient_basename": f"patient_{pid}" if pid not in ("Unknown", "nan", "", "0") else f"octdl_{disease}_{stem}", "study_meta": { "disease_label": disease, "eye": str(r.get("eye", "unknown")) if str(r.get("eye", "Unknown")) not in ("Unknown", "0") else "unknown", "patient_id": pid, "age": str(r.get("age", "Unknown")), "gender": str(r.get("gender", "Unknown")), "notes": r.get("notes", ""), "label_granularity": "b-scan", }, "slices": [{"src_path": src, "slice_idx": None}], }) return items # ============================================================ # OCTA500 enumerator (filename = volID-sliceID, 300 vol × 400) # ============================================================ def enum_octa500(in_root, octa500_subdir="OCTA500"): base = Path(in_root) / octa500_subdir images_dir = base / "images" labels_xlsx = base / "Text labels.xlsx" labels = {} if labels_xlsx.exists(): df = pd.read_excel(labels_xlsx) for _, r in df.iterrows(): labels[str(int(r["ID"]))] = { "disease": str(r["Disease"]).strip(), "sex": str(r["Sex"]).strip(), "eye": str(r["OS/OD"]).strip(), "age": str(r["Age"]).strip(), } # Group files by volume ID groups = defaultdict(list) for f in sorted(images_dir.glob("*.png")): m = re.match(r"^(\d+)-(\d+)\.png$", f.name) if not m: continue vol, slc = m.group(1), int(m.group(2)) groups[vol].append((slc, f)) items = [] for vol, files in groups.items(): files.sort() lab = labels.get(vol, {}) slices = [{"src_path": str(f), "slice_idx": i} for i, (_, f) in enumerate(files)] items.append({ "study_basename": f"vol_{vol}", "patient_basename": f"vol_{vol}", # 1 vol = 1 patient (no cross-vol patient ID) "study_meta": { "disease_label": lab.get("disease", "Unknown"), "eye": lab.get("eye", "unknown"), "patient_id": vol, "age": lab.get("age", "Unknown"), "gender": "M" if lab.get("sex") == "M" else ("F" if lab.get("sex") == "F" else "Unknown"), "label_granularity": "volume", "has_dc_mask": True, # OCTA500 has B-scan-level 6-class masks }, "slices": slices, }) return items def octa500_post_artifact(meta, sdir): """Copy 6-class B-scan masks for OCTA500.""" if not meta.get("has_dc_mask"): return mask_root = Path("/mnt/new/OCT Retinal B-scan数据集汇总/OCTA500/masks") for slc in meta["slices"]: idx = slc["idx"] src_fname = Path(slc["src_path"]).name # 10001-0001.png src_mask = mask_root / src_fname if src_mask.exists(): dst_mask = sdir / f"mask_{idx:03d}.png" save_mask_preserve(src_mask, dst_mask, force=False) def has_segmentation_octa500(meta, slc): idx = slc["idx"] sdir_parts = Path(meta["slices"][0]["src_path"]).parent # not used # The mask is saved post-hoc with mask_{idx:03d}.png inside study_dir. # has_segmentation is True if the corresponding mask file existed at source. src_fname = Path(slc["src_path"]).name mask_root = Path("/mnt/new/OCT Retinal B-scan数据集汇总/OCTA500/masks") return (mask_root / src_fname).exists() # ============================================================ # UESTC enumerator (3 sub-protocols) # ============================================================ def enum_uestc(in_root, uestc_subdir="UESTC天池"): base = Path(in_root) / uestc_subdir items = [] sub_to_protocol = { "Dataset_speckle_OCT_3D_6x6_split": ("BMizar 6x6mm", "uestc_bmizar_6x6"), "Dataset_speckle_OCT_3D_20x24_split": ("BMizar 20x24mm", "uestc_bmizar_20x24"), "Dataset_speckle_OCT_3D_Spectralis_split": ("Spectralis", "uestc_spectralis"), } for sub, (subset_name, subset_id) in sub_to_protocol.items(): sub_dir = base / sub if not sub_dir.exists(): continue files = sorted(sub_dir.glob("*.tif")) for f in files: stem = f.stem # e.g. 000000 dev_vendor = "spectralis_bmizar" if "bmizar" in subset_id else "heidelberg" dev_model = "bm_400k_bmizar" if "bmizar" in subset_id else "spectralis" items.append({ "study_basename": f"{subset_id}_{stem}", "patient_basename": f"{subset_id}_{stem}", # no patient grouping info "study_meta": { "disease_label": "Unknown", "eye": "unknown", "patient_id": None, "subset": subset_name, "subset_id": subset_id, "device_vendor": dev_vendor, "device_model": dev_model, "label_granularity": "b-scan", }, "slices": [{"src_path": str(f), "slice_idx": None}], }) return items def build_row_caps_uestc(meta, cohort, cohort_phrase): """UESTC override: scan_protocol per subset, severity always unknown.""" cfg = COHORT_CONFIG[cohort] sh = meta["study_hash"]; ph = meta["patient_hash"] eye = meta.get("eye", "unknown") subset = meta.get("subset_id", "") subset_label = meta.get("subset", "unknown") base = default_base_fields( cohort, sh, patient_hash=ph, eye=eye, ethnicity="Asian", hospital_domain="uestc_sichuan_v1") base["device_vendor"] = meta.get("device_vendor", "varies") base["device_model"] = meta.get("device_model", "varies") base["severity"] = "unknown" base["diagnosis_source"] = "none" # scan_protocol distinguishes subsets scan_protocol = { "uestc_bmizar_6x6": "volume_3d_macula_6x6mm", "uestc_bmizar_20x24": "volume_3d_macula_20x24mm", "uestc_spectralis": "volume_3d_macula_spectralis", }.get(subset, "volume_3d_macula") rows, caps = [], [] for slc in meta["slices"]: idx = slc["idx"] image_id = f"{cohort}_{sh}_bscan" file_path = rel_file_path(cohort, sh, "bscan.png") row = dict(base) row.update({ "image_id": image_id, "file_path": file_path, "file_format": "png", "modality": "oct_bscan", "anatomy": "macula", "device_technology": "sd_oct", "scan_protocol": scan_protocol, "bscan_index": idx, "image_height_px": slc["h"], "image_width_px": slc["w"], "has_segmentation": False, "n_layers_visible": 0, "is_valid": True, }) rows.append(row) caps.extend(caption_l1_oct(image_id, cohort_phrase, eye, device_vendor=row["device_vendor"], device_model=row["device_model"])) dev = device_phrase(row["device_vendor"], row["device_model"]) if dev: l3 = f"An OCT B-scan from {dev}, {cohort_phrase}, {subset_label} subset." else: l3 = f"An OCT B-scan from the {cohort_phrase}, {subset_label} subset." caps.append(caption_l3_oct(image_id, l3, "manifest_fields+subset")) return rows, caps # ============================================================ # Sidecar builders # ============================================================ def sidecar_demographics(meta): age = meta.get("age", "Unknown") gender = meta.get("gender", "Unknown") if age in (None, "Unknown", "", "nan") and gender in (None, "Unknown", "", "nan"): return None return { "study_id": meta["study_hash"], "patient_hash": meta["patient_hash"], "image_id_pattern": f"{meta['cohort']}_{meta['study_hash']}_bscan", "age": str(age), "gender": str(gender), "eye": meta.get("eye", "unknown"), "disease_label": meta.get("disease_label", "Unknown"), } # ============================================================ # Mask post-artifact helpers for datasets that have masks # ============================================================ def aroi_post_artifact(meta, sdir): """AROI mask: 6/AROI/AROI - online/24 patient/patientN/mask/""" src = Path(meta["slices"][0]["src_path"]) stem = src.stem # e.g. 6-patient1_raw0001 pid_m = re.match(r"6-(patient\d+)_raw(\d+)", stem) if not pid_m: return pid, raw_idx = pid_m.group(1), pid_m.group(2) mask_src = Path("/mnt/new/OCT Retinal B-scan数据集汇总/6/AROI/AROI - online/24 patient") / pid / "mask" / f"raw{raw_idx}.png" if mask_src.exists(): save_mask_preserve(mask_src, sdir / "layer_mask.png") def oimhs_post_artifact(meta, sdir): """OIMHS mask: 16/OIMHS dataset/output_layer/16-patient{eye_id}-{img_stem}_layer.png""" src = Path(meta["slices"][0]["src_path"]) eye_id = src.parent.name # Images/{eye_id}/file.png mask_src = Path(f"/mnt/new/OCT Retinal B-scan数据集汇总/16/OIMHS dataset/output_layer/16-patient{eye_id}-{src.stem}_layer.png") if mask_src.exists(): save_mask_preserve(mask_src, sdir / "layer_mask.png") def retouch_post_artifact(meta, sdir): """RETOUCH mask in parallel masks/ dir.""" for slc in meta["slices"]: src = Path(slc["src_path"]) mask_src = src.parent.parent / "masks" / src.name if mask_src.exists(): dst_name = "fluid_mask.png" if slc["idx"] is None else f"fluid_mask_{slc['idx']:03d}.png" save_mask_preserve(mask_src, sdir / dst_name) def amd_sd_post_artifact(meta, sdir): """AMD-SD mask: AMD-SD/masks/{filename}""" src = Path(meta["slices"][0]["src_path"]) mask_src = src.parent.parent / "masks" / src.name if mask_src.exists(): save_mask_preserve(mask_src, sdir / "lesion_mask.png") def chiu_post_artifact(meta, sdir): """Chiu DME masks (8 layers + fluid) — masks are in parallel dir if extracted by user.""" src = Path(meta["slices"][0]["src_path"]) mask_src = src.parent.parent / "masks" / src.name if mask_src.exists(): save_mask_preserve(mask_src, sdir / "layer_mask.png") # ============================================================ # Main dispatcher # ============================================================ CSV_NAME_TO_COHORT = { "Kermany": ("public_oct_kermany", None, None, None), "OCTID": ("public_oct_octid", None, None, None), "AROI": ("public_oct_aroi", None, None, aroi_post_artifact), "NEH_UT_2021": ("public_oct_neh_ut_2021", None, None, None), "AREDS2": ("public_oct_areds2", None, None, None), "Glaucoma_OCT": ("public_oct_glaucoma", None, None, None), "NYU_POAG": ("public_oct_nyu_poag", None, None, None), "OLIVES": ("public_oct_olives", enum_olives, None, None), "Chiu_DME_2015": ("public_oct_chiu_dme_2015", None, None, chiu_post_artifact), "Srinivasan_2014": ("public_oct_srinivasan_2014", None, None, None), "Sparsity_SDOCT_2012": ("public_oct_sparsity_sdoct_2012", None, None, None), "OIMHS": ("public_oct_oimhs", enum_oimhs_with_demographics, sidecar_demographics, oimhs_post_artifact), "RETOUCH": ("public_oct_retouch", enum_retouch, None, retouch_post_artifact), "THOCT1800": ("public_oct_thoct1800", None, None, None), "OCTDL": ("public_oct_octdl", enum_octdl_with_demographics, sidecar_demographics, None), "AMD-SD": ("public_oct_amd_sd", None, None, amd_sd_post_artifact), "C8": ("public_oct_c8", None, None, None), } def main(): ap = argparse.ArgumentParser() ap.add_argument("--input-root", required=True, help="Path to '/mnt/new/OCT Retinal B-scan数据集汇总'") ap.add_argument("--output-root", required=True, help="Output root (will create extracted/, manifest/, captions/)") ap.add_argument("--csv", default=None, help="Path to unified_metadata.csv (default: /数据分类整理汇总/unified_metadata.csv)") ap.add_argument("--cohorts", default="all", help="comma-separated cohort names (full or short like 'kermany,octa500'), or 'all'") ap.add_argument("--num-workers", type=int, default=8) ap.add_argument("--force", action="store_true") ap.add_argument("--limit-per-cohort", type=int, default=None, help="for testing: process only first N studies per cohort") args = ap.parse_args() in_root = Path(args.input_root) out_root = Path(args.output_root) csv_path = Path(args.csv) if args.csv else (in_root / "数据分类整理汇总" / "unified_metadata.csv") all_cohort_names = list(set(v[0] for v in CSV_NAME_TO_COHORT.values())) + [ "public_oct_octa500", "public_oct_uestc"] if args.cohorts == "all": cohorts_to_run = set(all_cohort_names) else: requested = [c.strip() for c in args.cohorts.split(",")] cohorts_to_run = set() for r in requested: for full in all_cohort_names: if full == r or full.endswith(f"_{r}") or r in full: cohorts_to_run.add(full) print(f"Will process {len(cohorts_to_run)} cohorts:") for c in sorted(cohorts_to_run): print(f" {c}") print() # CSV-driven A-class cohorts if any(v[0] in cohorts_to_run for v in CSV_NAME_TO_COHORT.values()): print(f"Loading CSV: {csv_path}") csv_df = pd.read_csv(csv_path, low_memory=False) print(f" {len(csv_df)} rows") for csv_name, (cohort, enum_fn, sidecar_fn, post_fn) in CSV_NAME_TO_COHORT.items(): if cohort not in cohorts_to_run: continue ds_df = csv_df[csv_df["dataset_name"] == csv_name] if len(ds_df) == 0: print(f"[{cohort}] no CSV rows for dataset {csv_name}, skipping") continue if enum_fn is None: items = enum_from_csv_simple(ds_df, in_root, csv_name) else: items = enum_fn(ds_df, in_root) if args.limit_per_cohort: items = items[:args.limit_per_cohort] run_cohort(cohort, items, _shared_build_row_caps, out_root, num_workers=args.num_workers, force=args.force, sidecar_fn=sidecar_fn, cohort_phrase=COHORT_CONFIG[cohort]["phrase"], post_artifact_fn=post_fn) # OCTA500 if "public_oct_octa500" in cohorts_to_run: items = enum_octa500(in_root) if args.limit_per_cohort: items = items[:args.limit_per_cohort] def build_octa500(meta, cohort, cohort_phrase): return _shared_build_row_caps( meta, cohort, cohort_phrase, has_segmentation_fn=has_segmentation_octa500, l3_extras_fn=lambda m, s: ["with B-scan-level 6-class segmentation mask"] if has_segmentation_octa500(m, s) else [], ) run_cohort("public_oct_octa500", items, build_octa500, out_root, num_workers=args.num_workers, force=args.force, sidecar_fn=sidecar_demographics, cohort_phrase=COHORT_CONFIG["public_oct_octa500"]["phrase"], post_artifact_fn=octa500_post_artifact) # UESTC if "public_oct_uestc" in cohorts_to_run: items = enum_uestc(in_root) if args.limit_per_cohort: items = items[:args.limit_per_cohort] run_cohort("public_oct_uestc", items, build_row_caps_uestc, out_root, num_workers=args.num_workers, force=args.force, sidecar_fn=None, cohort_phrase=COHORT_CONFIG["public_oct_uestc"]["phrase"], post_artifact_fn=None) print("\n[main] all done.") if __name__ == "__main__": main()