Add All Folders

Luigi/engine/flags.py

@@ -0,0 +1,20 @@
+import os
+
+def flags_dir(workspace: str) -> str:
+    d = os.path.join(workspace, "_flags")
+    os.makedirs(d, exist_ok=True)
+    return d
+
+def flag_path(workspace: str, name: str) -> str:
+    return os.path.join(flags_dir(workspace), name)
+
+def is_set(workspace: str, name: str) -> bool:
+    return os.path.exists(flag_path(workspace, name))
+
+def set_flag(workspace: str, name: str) -> None:
+    open(flag_path(workspace, name), "a").close()
+
+def clear_flag(workspace: str, name: str) -> None:
+    p = flag_path(workspace, name)
+    if os.path.exists(p):
+        os.remove(p)

Luigi/engine/pipeline.py

@@ -0,0 +1,19 @@
+import luigi
+import time
+from engine.tasks_writer import ImageWriter
+
+class RadiumaPipeline(luigi.WrapperTask):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        # start timer at the very beginning
+        self.start_time = time.time()
+        return ImageWriter(artifacts_dir=self.artifacts_dir)
+
+    def run(self):
+        elapsed = time.time() - self.start_time
+        hours, rem = divmod(elapsed, 3600)
+        minutes, seconds = divmod(rem, 60)
+        centiseconds = int((seconds - int(seconds)) * 100)
+        print(f"[Pipeline] total execution time: {int(hours):02}:{int(minutes):02}:{int(seconds):02}.{centiseconds:02}")
+        print("=== Workflow completed successfully ===")

Luigi/engine/provenance.py

@@ -0,0 +1,26 @@
+import os, time, json, hashlib
+from typing import Iterable
+
+def sha256_file(fp: str) -> str:
+    h = hashlib.sha256()
+    with open(fp, "rb") as f:
+        for chunk in iter(lambda: f.read(1<<20), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+def write_sidecar(outputs: Iterable, params: dict) -> None:
+    outs = list(outputs)
+    if not outs: return
+    prov_path = os.path.join(os.path.dirname(outs[0].path), "_provenance.json")
+    meta = {
+        "params": params,
+        "tool": {"name": "Radiuma-Luigi", "version": params.get("tool_version", "0.1.0")},
+        "timestamps": {"finished_at": time.strftime("%Y-%m-%d %H:%M:%S")},
+        "checksums": {}
+    }
+    for o in outs:
+        p = o.path
+        if os.path.exists(p):
+            meta["checksums"][os.path.basename(p)] = sha256_file(p)
+    with open(prov_path, "w", encoding="utf-8") as f:
+        json.dump(meta, f, indent=2)

Luigi/engine/tasks_conversion.py

@@ -0,0 +1,29 @@
+import os
+import luigi
+import SimpleITK as sitk
+from pathlib import Path
+
+class ImageConversion(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        from engine.tasks_fusion import ImageFusion
+        return ImageFusion(artifacts_dir=self.artifacts_dir)
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "converted_index.txt"))
+
+    def run(self):
+        fused_index = os.path.join(self.artifacts_dir, "fused_index.txt")
+        with open(fused_index, "r") as f:
+            fused_paths = [line.strip() for line in f if line.strip()]
+
+        converted_paths = []
+        for img_path in fused_paths:
+            img = sitk.ReadImage(img_path)
+            out_path = Path(self.artifacts_dir) / f"converted_{Path(img_path).name}"
+            sitk.WriteImage(img, str(out_path))
+            converted_paths.append(str(out_path))
+
+        with self.output().open("w") as f:
+            f.write("\n".join(converted_paths))

Luigi/engine/tasks_filter.py

@@ -0,0 +1,31 @@
+import os
+import luigi
+import SimpleITK as sitk
+from pathlib import Path
+
+class ImageFilter(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+    sigma = luigi.FloatParameter(default=1.0)
+
+    def requires(self):
+        from engine.tasks_conversion import ImageConversion
+        return ImageConversion(artifacts_dir=self.artifacts_dir)
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "filtered_index.txt"))
+
+    def run(self):
+        conv_index = os.path.join(self.artifacts_dir, "converted_index.txt")
+        with open(conv_index, "r") as f:
+            conv_paths = [line.strip() for line in f if line.strip()]
+
+        filtered_paths = []
+        for img_path in conv_paths:
+            img = sitk.ReadImage(img_path)
+            filtered_img = sitk.SmoothingRecursiveGaussian(img, sigma=self.sigma)
+            out_path = Path(self.artifacts_dir) / f"filtered_{Path(img_path).name}"
+            sitk.WriteImage(filtered_img, str(out_path))
+            filtered_paths.append(str(out_path))
+
+        with self.output().open("w") as f:
+            f.write("\n".join(filtered_paths))

Luigi/engine/tasks_fusion.py

@@ -0,0 +1,36 @@
+import os
+import luigi
+import numpy as np
+import SimpleITK as sitk
+from pathlib import Path
+
+class ImageFusion(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        from engine.tasks_registration import ImageRegistration
+        return ImageRegistration(artifacts_dir=self.artifacts_dir)
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "fused_index.txt"))
+
+    def run(self):
+        reg_index = os.path.join(self.artifacts_dir, "registered_index.txt")
+        with open(reg_index, "r") as f:
+            reg_paths = [line.strip() for line in f if line.strip()]
+
+        fused_paths = []
+        for img_path in reg_paths:
+            img = sitk.ReadImage(img_path)
+            arr = sitk.GetArrayFromImage(img)
+            p5, p95 = np.percentile(arr, [5, 95])
+            arr = np.clip(arr, p5, p95)
+            arr = (arr - p5) / (p95 - p5) if p95 > p5 else arr * 0.0
+            fused_img = sitk.GetImageFromArray(arr)
+            fused_img.CopyInformation(img)
+            out_path = Path(self.artifacts_dir) / f"fused_{Path(img_path).name}"
+            sitk.WriteImage(fused_img, str(out_path))
+            fused_paths.append(str(out_path))
+
+        with self.output().open("w") as f:
+            f.write("\n".join(fused_paths))

Luigi/engine/tasks_maskreg.py

@@ -0,0 +1,49 @@
+import os
+import luigi
+import SimpleITK as sitk
+from pathlib import Path
+
+class MaskRegistration(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+    mask_dir = luigi.Parameter(default=os.path.join("data", "masks"))
+
+    def requires(self):
+        from engine.tasks_filter import ImageFilter
+        from engine.tasks_masks import AllMasks
+        return {
+            "filter": ImageFilter(artifacts_dir=self.artifacts_dir),
+            "masks": AllMasks(mask_dir=self.mask_dir)
+        }
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "mask_registered_index.txt"))
+
+    def run(self):
+        filt_index = os.path.join(self.artifacts_dir, "filtered_index.txt")
+        with open(filt_index, "r") as f:
+            filtered_paths = [line.strip() for line in f if line.strip()]
+
+        mask_files = [os.path.join(self.mask_dir, f) for f in os.listdir(self.mask_dir) if f.endswith(".nii.gz")]
+        if not filtered_paths or not mask_files:
+            raise FileNotFoundError("Missing filtered images or masks for mask_registration.")
+
+        if len(filtered_paths) == len(mask_files):
+            pairs = zip(filtered_paths, mask_files)
+        else:
+            ref_path = filtered_paths[0]
+            pairs = [(ref_path, m) for m in mask_files]
+
+        out_paths = []
+        for ref_img_path, mask_path in pairs:
+            ref_img = sitk.ReadImage(ref_img_path)
+            mask_img = sitk.ReadImage(mask_path)
+            identity = sitk.Transform(ref_img.GetDimension(), sitk.sitkIdentity)
+            resampled_mask = sitk.Resample(
+                mask_img, ref_img, identity, sitk.sitkNearestNeighbor, 0, mask_img.GetPixelID()
+            )
+            out_path = Path(self.artifacts_dir) / f"mask_registered_{Path(mask_path).name}"
+            sitk.WriteImage(resampled_mask, str(out_path))
+            out_paths.append(str(out_path))
+
+        with self.output().open("w") as f:
+            f.write("\n".join(out_paths))

Luigi/engine/tasks_masks.py

@@ -0,0 +1,17 @@
+import os
+import luigi
+
+class AllMasks(luigi.Task):
+    mask_dir = luigi.Parameter(default=os.path.join("data", "masks"))
+
+    def output(self):
+        # Merely as a signal of completion
+        return luigi.LocalTarget(os.path.join("artifacts", "all_masks.done"))
+
+    def run(self):
+        files = [os.path.join(self.mask_dir, f) for f in os.listdir(self.mask_dir) if f.endswith(".nii.gz")]
+        if not files:
+            raise FileNotFoundError("No masks found in data/masks")
+        # Just make the done signal.
+        with self.output().open("w") as f:
+            f.write(f"{len(files)} masks discovered")

Luigi/engine/tasks_processing.py

@@ -0,0 +1,107 @@
+import luigi
+import json
+from pathlib import Path
+import numpy as np
+import SimpleITK as sitk
+from engine.tasks_reader import ImageReader
+from engine.utils import ensure_dir
+
+class ImageFusion(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageReader(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"fusion_{stem}.json"))
+
+    def run(self):
+        # If we had the second modality, we would read here and stack the channels.
+        # For now, we're passing that single image along with the metadata.
+        payload = {"status": "fused", "modalities": 1, "image": str(self.image_file)}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class ImageConversion(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageFusion(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"conversion_{stem}.json"))
+
+    def run(self):
+        # Convert to SimpleITK image for later steps
+        sitk_img = sitk.ReadImage(str(self.image_file))
+        # Type conversion/normalization
+        payload = {"status": "converted", "pixel_type": str(sitk_img.GetPixelIDTypeAsString())}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class ImageFilter(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+    sigma = luigi.FloatParameter(default=1.0)
+
+    def requires(self):
+        return ImageConversion(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"filter_{stem}.json"))
+
+    def run(self):
+        img = sitk.ReadImage(str(self.image_file))
+        # Gaussian smoothing for ROI preparation
+        filtered = sitk.DiscreteGaussian(img, variance=self.sigma ** 2)
+        # Store Data
+        payload = {"status": "filtered", "sigma": self.sigma}
+        with self.output().open("w") as f:
+            json.dump(payload, f, indent=2)
+
+
+class MaskRegistration(luigi.Task):
+    image_file = luigi.Parameter()
+    mask_file = luigi.Parameter(default="")
+    workspace = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        return ImageFilter(image_file=self.image_file, mask_file=self.mask_file, workspace=self.workspace)
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.workspace) / "pipeline")
+        stem = Path(self.image_file).stem
+        return luigi.LocalTarget(str(out_dir / f"maskreg_{stem}.json"))
+
+    def run(self):
+        # If we have a mask, we register/resample to image space.
+        result = {"status": "mask_registered", "mask_available": bool(self.mask_file)}
+        if self.mask_file:
+            img = sitk.ReadImage(str(self.image_file))
+            msk = sitk.ReadImage(str(self.mask_file))
+            # Resample mask to image geometry
+            resampler = sitk.ResampleImageFilter()
+            resampler.SetReferenceImage(img)
+            resampler.SetInterpolator(sitk.sitkNearestNeighbor)
+            resampler.SetDefaultPixelValue(0)
+            msk_res = resampler.Execute(msk)
+            # Temporary storage of PySera results
+            tmp_dir = ensure_dir(Path(self.workspace) / "tmp")
+            out_mask = Path(tmp_dir) / f"regmask_{Path(self.image_file).stem}.nii.gz"
+            sitk.WriteImage(msk_res, str(out_mask))
+            result["registered_mask_path"] = str(out_mask)
+        with self.output().open("w") as f:
+            json.dump(result, f, indent=2)

Luigi/engine/tasks_radiomics.py

@@ -0,0 +1,89 @@
+import os
+import time
+import json
+import luigi
+from pathlib import Path
+import pysera
+from engine.utils import json_safe
+
+class FeatureExtraction(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+    temp_dir = luigi.Parameter(default=r"C:\Users\Omen16\AppData\Local\ViSERA\res\memory\memmap\pysera_temp")
+
+    def requires(self):
+        from engine.tasks_filter import ImageFilter
+        from engine.tasks_maskreg import MaskRegistration
+        return {
+            "filter": ImageFilter(artifacts_dir=self.artifacts_dir),
+            "maskreg": MaskRegistration(artifacts_dir=self.artifacts_dir)
+        }
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "radiomics_index.json"))
+
+    def run(self):
+        # 
+        filt_index = os.path.join(self.artifacts_dir, "filtered_index.txt")
+        mask_index = os.path.join(self.artifacts_dir, "mask_registered_index.txt")
+        with open(filt_index, "r") as f:
+            filtered_paths = [line.strip() for line in f if line.strip()]
+        with open(mask_index, "r") as f:
+            mask_paths = [line.strip() for line in f if line.strip()]
+
+        results = []
+        for img, mask in zip(filtered_paths, mask_paths):
+            start = time.time()
+            result = pysera.process_batch(
+                image_input=img,
+                mask_input=mask,
+                output_path=self.artifacts_dir,
+                categories="diag,morph,glcm,glrlm,glszm,ngtdm,ngldm",
+                dimensions="1st,3D",
+                bin_size=25,
+                roi_num=2,
+                roi_selection_mode="per_region",
+                apply_preprocessing=True,
+                feature_value_mode="REAL_VALUE",
+                min_roi_volume=50,
+                enable_parallelism=True,
+                num_workers="4",
+                report="info",
+                temporary_files_path=str(self.temp_dir),
+                IBSI_based_parameters={
+                    "radiomics_DataType": "CT",
+                    "radiomics_DiscType": "FBS",
+                    "radiomics_isScale": 0,
+                    "radiomics_VoxInterp": "Nearest",
+                    "radiomics_ROIInterp": "Nearest",
+                    "radiomics_isotVoxSize": 2.0,
+                    "radiomics_isotVoxSize2D": 2.0,
+                    "radiomics_isIsot2D": 0,
+                    "radiomics_isGLround": 0,
+                    "radiomics_isReSegRng": 0,
+                    "radiomics_isOutliers": 0,
+                    "radiomics_isQuntzStat": 1,
+                    "radiomics_ReSegIntrvl01": -1000,
+                    "radiomics_ReSegIntrvl02": 400,
+                    "radiomics_ROI_PV": 0.5,
+                    "radiomics_qntz": "Uniform",
+                    "radiomics_IVH_Type": 3,
+                    "radiomics_IVH_DiscCont": 1,
+                    "radiomics_IVH_binSize": 2.0,
+                },
+            )
+            elapsed = round(time.time() - start, 2)
+
+            safe_result = json_safe(result)
+            case_json = os.path.join(self.artifacts_dir, f"{Path(img).name}_radiomics.json")
+            with open(case_json, "w", encoding="utf-8") as f:
+                json.dump(safe_result, f, indent=2, ensure_ascii=False)
+
+            results.append({
+                "image": img,
+                "mask": mask,
+                "elapsed_seconds": elapsed,
+                "result_file": case_json,
+            })
+
+        with self.output().open("w") as f:
+            json.dump({"radiomics_results": results}, f, indent=2, ensure_ascii=False)

Luigi/engine/tasks_reader.py

@@ -0,0 +1,28 @@
+import os
+import luigi
+import SimpleITK as sitk
+from pathlib import Path
+from engine.utils import ensure_dir
+
+class ImageReader(luigi.Task):
+    data_dir = luigi.Parameter(default=os.path.join("data", "images"))
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def output(self):
+        out_dir = ensure_dir(Path(self.artifacts_dir) / "reader")
+        return luigi.LocalTarget(str(out_dir / "reader_index.txt"))
+
+    def run(self):
+        reader_dir = ensure_dir(Path(self.artifacts_dir) / "reader")
+        files = [os.path.join(self.data_dir, f) for f in os.listdir(self.data_dir) if f.endswith(".nii.gz")]
+        if not files:
+            raise FileNotFoundError("No images found in data/images")
+        converted_paths = []
+        for path in files:
+            img = sitk.ReadImage(path)
+            img_float = sitk.Cast(img, sitk.sitkFloat32)
+            out_path = reader_dir / f"reader_{Path(path).name}"
+            sitk.WriteImage(img_float, str(out_path))
+            converted_paths.append(str(out_path))
+        with self.output().open("w") as f:
+            f.write("\n".join(converted_paths))

Luigi/engine/tasks_registration.py

@@ -0,0 +1,73 @@
+import os
+import luigi
+import SimpleITK as sitk
+from pathlib import Path
+from engine.utils import ensure_dir
+
+def cast_to_float32(img: sitk.Image) -> sitk.Image:
+    return sitk.Cast(img, sitk.sitkFloat32)
+
+def make_initial_transform(fixed: sitk.Image, moving: sitk.Image) -> sitk.Transform:
+    dim = fixed.GetDimension()
+    if dim == 2:
+        return sitk.CenteredTransformInitializer(
+            fixed, moving, sitk.Euler2DTransform(), sitk.CenteredTransformInitializerFilter.GEOMETRY
+        )
+    elif dim == 3:
+        return sitk.CenteredTransformInitializer(
+            fixed, moving, sitk.VersorRigid3DTransform(), sitk.CenteredTransformInitializerFilter.GEOMETRY
+        )
+    else:
+        raise RuntimeError(f"Unsupported image dimension: {dim}")
+
+class ImageRegistration(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        from engine.tasks_reader import ImageReader
+        return ImageReader(artifacts_dir=self.artifacts_dir)
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "registered_index.txt"))
+
+    def run(self):
+        # Reader Path Reader
+        reader_index = os.path.join(self.artifacts_dir, "reader", "reader_index.txt")
+        with open(reader_index, "r") as f:
+            reader_paths = [line.strip() for line in f if line.strip()]
+
+        fixed_raw = sitk.ReadImage(reader_paths[0])
+        fixed = cast_to_float32(fixed_raw)
+
+        R = sitk.ImageRegistrationMethod()
+        if fixed.GetDimension() == 2:
+            R.SetMetricAsMeanSquares()
+            R.SetInterpolator(sitk.sitkLinear)
+        else:
+            R.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)
+            R.SetMetricSamplingStrategy(R.RANDOM)
+            R.SetMetricSamplingPercentage(0.2)
+            R.SetInterpolator(sitk.sitkLinear)
+
+        R.SetOptimizerAsRegularStepGradientDescent(
+            learningRate=2.0, minStep=1e-4, numberOfIterations=200, gradientMagnitudeTolerance=1e-8
+        )
+        R.SetOptimizerScalesFromPhysicalShift()
+        R.SetShrinkFactorsPerLevel(shrinkFactors=[4, 2, 1])
+        R.SetSmoothingSigmasPerLevel(smoothingSigmas=[2, 1, 0])
+        R.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()
+
+        out_paths = []
+        for img_path in reader_paths:
+            moving_raw = sitk.ReadImage(img_path)
+            moving = cast_to_float32(moving_raw)
+            init_tx = make_initial_transform(fixed, moving)
+            R.SetInitialTransform(init_tx, inPlace=False)
+            final_tx = R.Execute(fixed, moving)
+            registered = sitk.Resample(moving, fixed, final_tx, sitk.sitkLinear, 0.0, sitk.sitkFloat32)
+            out_path = Path(self.artifacts_dir) / f"registered_{Path(img_path).name}"
+            sitk.WriteImage(registered, str(out_path))
+            out_paths.append(str(out_path))
+
+        with self.output().open("w") as f:
+            f.write("\n".join(out_paths))

Luigi/engine/tasks_writer.py

@@ -0,0 +1,33 @@
+import json
+import os
+from pathlib import Path
+import shutil
+import luigi
+from engine.utils import ensure_dir
+
+
+class ImageWriter(luigi.Task):
+    artifacts_dir = luigi.Parameter(default="artifacts")
+
+    def requires(self):
+        from engine.tasks_radiomics import FeatureExtraction
+        return FeatureExtraction(artifacts_dir=self.artifacts_dir)
+
+    def output(self):
+        return luigi.LocalTarget(os.path.join(self.artifacts_dir, "final_output.json"))
+
+    def run(self):
+        # Copy Excel file generated by PySERA to artifacts/radiomics3d
+        excel_src = Path(self.artifacts_dir) / "Radiomics_Results.xlsx"
+        if excel_src.exists():
+            dst_dir = ensure_dir(Path(self.artifacts_dir) / "radiomics3d")
+            excel_dst = dst_dir / "Radiomics_Results.xlsx"
+            shutil.copy2(excel_src, excel_dst)
+
+        # Load summary JSON from FeatureExtraction
+        with self.requires().output().open("r") as f:
+            summary = json.load(f)
+
+        # Write final summary JSON
+        with self.output().open("w") as f:
+            json.dump(summary, f, indent=2, ensure_ascii=False)

Luigi/engine/utils.py

@@ -0,0 +1,27 @@
+from pathlib import Path
+import json
+import pandas as pd
+
+def ensure_dir(p):
+    p = Path(p)
+    p.mkdir(parents=True, exist_ok=True)
+    return p
+
+def json_safe(obj):
+    from pathlib import Path
+    import numpy as np
+    import pandas as pd
+
+    if isinstance(obj, Path):
+        return str(obj)
+    if isinstance(obj, (str, int, float, bool)) or obj is None:
+        return obj
+    if isinstance(obj, dict):
+        return {str(k): json_safe(v) for k, v in obj.items()}
+    if isinstance(obj, (list, tuple)):
+        return [json_safe(v) for v in obj]
+    if isinstance(obj, pd.DataFrame):
+        return obj.to_dict(orient="records")
+    if isinstance(obj, np.ndarray):
+        return obj.tolist()
+    return obj  # fallback