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Phase I/To_Dicom.py

@@ -0,0 +1,12 @@
+import pyplastimatch as pypla
+from pyplastimatch.utils.install import install_precompiled_binaries
+
+install_precompiled_binaries("")
+
+# convert one of the NIFTI images to DICOM: name: <patient1>, output folder: <dicom_output>
+convert_args_ct = {
+    "input": "datasets/004428_01_02_182-242.nii.gz",
+    "patient-id": "patient1",
+    "output-dicom": "dicom_output",
+}
+pypla.convert(verbose=True, **convert_args_ct)

Phase I/generateData.py

@@ -0,0 +1,225 @@
+import numpy as np
+import nibabel as nib
+import os
+import cv2
+import csv
+import nibabel as nib
+import pydicom
+from pydicom.dataset import FileDataset
+import numpy as np
+import os
+import datetime
+import zipfile
+
+def slices2nifti(ims, fn_out, spacing):  
+    """Save 2D slices to 3D NIfTI file considering the spacing."""  
+    if len(ims) < 300:  # cv2.merge does not support too many channels  
+        V = cv2.merge(ims)  
+    else:  
+        V = np.empty((ims[0].shape[0], ims[0].shape[1], len(ims)))  
+        for i in range(len(ims)):  
+            V[:, :, i] = ims[i]  
+
+    # The transformation matrix suitable for 3D slicer and ITK-SNAP  
+    T = np.array([[0, -spacing[1], 0, 0],   
+                  [-spacing[0], 0, 0, 0],   
+                  [0, 0, -spacing[2], 0],   
+                  [0, 0, 0, 1]])  
+    img = nib.Nifti1Image(V, T)  
+    path_out = os.path.join(dir_out, fn_out)  
+    nib.save(img, path_out)  
+    return path_out 
+
+
+
+def load_slices(dir, slice_idxs):
+    """Load slices from 16-bit PNG files and return images with their filenames."""
+    slice_idxs = np.array(slice_idxs)
+    if not np.all(slice_idxs[1:] - slice_idxs[:-1] == 1):
+        print(f"⚠️ Slice indices are not consecutive")
+
+
+    ims = []
+    filenames = []
+
+    for slice_idx in slice_idxs:
+        fn = f'{slice_idx:03d}.png'
+        path = os.path.join(dir_in, dir, fn)
+        im = cv2.imread(path, -1)  # -1 to preserve 16-bit depth
+        assert im is not None, f'Error reading {path}'
+
+        im_corrected = (im.astype(np.int32) - 32768).astype(np.int16)
+        ims.append(im_corrected)
+        filenames.append(fn.split('.')[0])
+
+    return ims, filenames
+ 
+
+def read_DL_info():  
+    """Read spacings and image indices in DeepLesion."""  
+    spacings = []  
+    idxs = []  
+    with open(info_fn, 'r') as csvfile:  # Use 'r' mode for reading text files  
+        reader = csv.reader(csvfile)  
+        rownum = 0  
+        for row in reader:  
+            if rownum == 0:  
+                header = row  
+                rownum += 1  
+            else:  
+                idxs.append([int(d) for d in row[1:4]])  
+                spacings.append([float(d) for d in row[12].split(',')])  
+
+    idxs = np.array(idxs)  
+    spacings = np.array(spacings)  
+    return idxs, spacings  
+
+def nii_to_dicom(nii_path, output_folder, filenames):
+    nii_img = nib.load(nii_path)
+    data = nii_img.get_fdata()
+    affine = nii_img.affine
+    num_slices = data.shape[2]
+
+    assert len(filenames) == num_slices, "Length of filename list must match number of slices."
+
+    nii_base = os.path.splitext(os.path.basename(nii_path))[0]
+    nii_base = nii_base.split('.')[0]
+    dicom_subfolder = os.path.join(output_folder, nii_base)
+    os.makedirs(dicom_subfolder, exist_ok=True)
+
+    for i in range(num_slices):
+        filename = os.path.join(dicom_subfolder, filenames[i])+'.dcm'  # Use provided filename
+
+        file_meta = pydicom.Dataset()
+        file_meta.MediaStorageSOPClassUID = pydicom.uid.SecondaryCaptureImageStorage
+        file_meta.MediaStorageSOPInstanceUID = pydicom.uid.generate_uid()
+        file_meta.ImplementationClassUID = "1.2.3.4.5.6.7.8.9.0"
+        file_meta.TransferSyntaxUID = pydicom.uid.ExplicitVRLittleEndian
+
+        ds = FileDataset(filename, {}, file_meta=file_meta, preamble=b"\0" * 128)
+
+        dt = datetime.datetime.now()
+        ds.PatientName = "Test^Patient"
+        ds.PatientID = "123456"
+        ds.Modality = "MR"
+        ds.StudyInstanceUID = pydicom.uid.generate_uid()
+        ds.SeriesInstanceUID = pydicom.uid.generate_uid()
+        ds.SOPInstanceUID = file_meta.MediaStorageSOPInstanceUID
+        ds.SOPClassUID = file_meta.MediaStorageSOPClassUID
+        ds.StudyDate = dt.strftime('%Y%m%d')
+        ds.StudyTime = dt.strftime('%H%M%S')
+
+        ds.Rows, ds.Columns = data.shape[:2]
+        ds.InstanceNumber = i + 1
+        ds.ImagePositionPatient = [float(affine[0,3]), float(affine[1,3]), float(affine[2,3] + i)]
+        ds.ImageOrientationPatient = [1,0,0,0,1,0]
+        ds.PixelSpacing = [1.0, 1.0]
+        ds.SliceThickness = 1.0
+        ds.SamplesPerPixel = 1
+        ds.PhotometricInterpretation = "MONOCHROME2"
+        ds.BitsAllocated = 16
+        ds.BitsStored = 16
+        ds.HighBit = 15
+        ds.PixelRepresentation = 1
+
+        pixel_array = data[:, :, i].astype(np.uint16)
+        ds.PixelData = pixel_array.tobytes()
+
+        ds.is_little_endian = True
+        ds.is_implicit_VR = False
+        ds.save_as(filename)
+
+
+def extract_and_collect_main_folders(zip_root_dir):
+    extracted_paths = []
+
+    for zip_name in sorted(os.listdir(zip_root_dir)):
+        zip_path = os.path.join(zip_root_dir, zip_name)
+
+        # فقط فایل‌های .zip واقعی
+        if zip_name.lower().endswith('.zip') and os.path.isfile(zip_path):
+            try:
+                extract_folder = os.path.join(zip_root_dir, zip_name.replace('.zip', ''))
+                os.makedirs(extract_folder, exist_ok=True)
+
+                with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+                    zip_ref.extractall(extract_folder)
+
+                os.remove(zip_path)
+
+                # حذف پوشه‌های مخفی
+                extracted_subfolders = [
+                    os.path.join(extract_folder, name)
+                    for name in os.listdir(extract_folder)
+                    if os.path.isdir(os.path.join(extract_folder, name)) and not name.startswith('.')
+                ]
+
+                if not extracted_subfolders:
+                    extracted_paths.append(extract_folder)
+                else:
+                    extracted_paths.extend(extracted_subfolders)
+
+            except zipfile.BadZipFile:
+                print(f"⚠️ Skipping bad zip file: {zip_name}")
+                continue
+
+    return extracted_paths
+
+
+# Main
+zip_root_dir ='' 
+folders = extract_and_collect_main_folders(zip_root_dir)
+def find_image_folders(directory):  
+    
+    image_folders = []  
+    
+    # 
+    for root, dirs, files in os.walk(directory):  
+        for dir_name in dirs:  
+            if dir_name.startswith("Images_png_"):  
+                
+                image_folders.append(os.path.join(root, dir_name))  
+    
+    return image_folders  
+
+ 
+folder_path = ''   
+result = find_image_folders(folder_path)  
+
+print(result)
+
+dir_out = ''
+out_fmt = '%s.nii.gz'  # format of the nifti file name to output
+info_fn =''  # file name of the information file
+idxs, spacings = read_DL_info()  
+
+for folder in result :
+    dir_in = folder + '/Images_png'
+    if not os.path.exists(dir_out):  
+        os.mkdir(dir_out)  
+    img_dirs = os.listdir(dir_in)  
+    img_dirs.sort() 
+
+    for dir1 in img_dirs:  
+        #Find the image info according to the folder's name    
+
+        idxs1 = np.array([int(d) for d in dir1.split('_')])  
+        i1 = np.where(np.all(idxs == idxs1, axis=1))[0]  
+        spacings1 = spacings[i1[0]]  
+
+        fns = os.listdir(os.path.join(dir_in, dir1))  
+        slices = [int(d[:-4]) for d in fns if d.endswith('.png')]  
+        slices.sort()  
+
+        groups = [slices] 
+
+        for group in groups:  
+            # Group contains slices indices of a sub-volume  
+            ims,names = load_slices(dir1, group)  
+            fn_out = out_fmt % (dir1)  
+            path_out = slices2nifti(ims, fn_out, spacings1) 
+            nii_to_dicom(path_out, "",names)
+
+
+
+

Phase I/generate_mask.py

@@ -0,0 +1,77 @@
+import os
+import nibabel as nib
+import numpy as np
+import cv2
+import glob
+from natsort import natsorted
+from organList import *  # لیست اندام‌ها مانند Organ = ['liver', 'spleen', ...]
+
+# اندام‌های مورد نظر برای استخراج ماسک
+target_organs = ["liver", "spleen", "kidney_right", "kidney_left", "gallbladder", "stomach", "pancreas"]
+
+# مسیرها
+NIFTI_data_dir = '/media/external_10T/mehran_advand/segment/myenv/monai_wholeBody_ct_segmentation/Segmentation_Output'
+DCM_data_dir = '/media/external_10T/mehran_advand/DeepLesion/Images_dicom_test'
+output_dir = 'MONAI/'
+
+# دریافت لیست بیماران (فولدرهایی مثل 000001_01_01)
+patient_folders = natsorted(os.listdir(NIFTI_data_dir))
+
+for patient_id in patient_folders:
+
+    # if patient_id != '000053_06_01':
+    #      continue
+
+    print(f"\n🧾 Processing patient: {patient_id}")
+    
+    # ساخت مسیر فایل NIfTI و فولدر DICOM
+    nii_path = os.path.join(NIFTI_data_dir, patient_id, f"{patient_id}_trans.nii.gz")
+    dcm_folder = os.path.join(DCM_data_dir, patient_id)
+    
+    if not os.path.isfile(nii_path):
+        print(f"❌ NIfTI file not found for {patient_id}")
+        continue
+
+    dcm_files = glob.glob(os.path.join(dcm_folder, "*.dcm"))
+    if not dcm_files:
+        print(f"❌ No DICOM files found for {patient_id}")
+        continue
+    
+    dcm_files = natsorted(dcm_files)
+    
+    # بارگذاری داده‌ی NIfTI
+    print(f"📥 Reading NIfTI: {nii_path}")
+    nii = nib.load(nii_path)
+    label_data = nii.get_fdata()
+
+    # بررسی و تصحیح محورها بر اساس affine
+    if nii.affine[0, 0] > 0:
+        label_data = np.flip(label_data, axis=0)
+    if nii.affine[1, 1] > 0:
+        label_data = np.flip(label_data, axis=1)
+    if nii.affine[2, 2] > 0:
+        label_data = np.flip(label_data, axis=2)
+
+    label_data = np.transpose(label_data, (2, 1, 0))  # Z, Y, X
+    num_slices = min(len(label_data), len(dcm_files))
+
+    # پردازش هر اندام
+    for target_organ in target_organs:
+        if target_organ not in Organ:
+            print(f"⚠️ Organ '{target_organ}' not in Organ list. Skipping...")
+            continue
+
+        organ_index = Organ.index(target_organ)
+        print(f"🧠 Processing organ: {target_organ} (index {organ_index})")
+
+        for idx in range(num_slices):
+            binary_mask = (label_data[idx] == organ_index).astype(np.uint8) * 255
+            #if np.any(binary_mask):
+            dcm_path = dcm_files[idx]
+            out_path = dcm_path.replace(DCM_data_dir, output_dir)
+            out_path = out_path.replace(patient_id, f"{patient_id}/MONAI_{target_organ}")
+            out_path = out_path.replace('.dcm', '_OUT.png')
+            out_path = out_path.replace('\\', '/')
+
+            os.makedirs(os.path.dirname(out_path), exist_ok=True)
+            cv2.imwrite(out_path, binary_mask)

Phase I/lesion_report.py

@@ -0,0 +1,127 @@
+import os
+import pandas as pd
+import cv2
+import numpy as np
+from tqdm import tqdm
+
+SEG_ROOT = "MONAI"
+DL_INFO_PATH = ""
+OUTPUT_PATH = ""
+DEBUG_DIR = ""
+os.makedirs(DEBUG_DIR, exist_ok=True)
+
+TARGET_ORGANS = ["liver", "spleen", "kidney_right", "kidney_left", "gallbladder", "stomach", "pancreas"]
+OVERLAP_THRESHOLD = 0.1
+
+df = pd.read_csv(DL_INFO_PATH)
+results = []
+
+
+for idx, row in tqdm(df.iterrows(), total=len(df)):
+    try:
+        filename = row["File_name"]
+        patient_id, study_id, series_id = filename.split("_")[:3]
+        series_name = f"{patient_id}_{study_id}_{series_id}"
+        series_path = os.path.join(SEG_ROOT, series_name)
+
+        if not os.path.exists(series_path):
+            continue
+
+        bbox = np.array(row["Bounding_boxes"].strip("[]").split(","), dtype=float).astype(int).tolist()
+        bbox_x1, bbox_y1, bbox_x2, bbox_y2 = bbox
+        lesion_area = (bbox_x2 - bbox_x1) * (bbox_y2 - bbox_y1)
+        if lesion_area == 0:
+            continue
+
+        slice_start, slice_end = map(int, str(row["Slice_range"]).strip().split(","))
+        key_slice = int(row["Key_slice_index"])
+        matched_organs = set()
+        found_overlap = False
+
+        overlap_slices = range(max(key_slice - 2, slice_start), min(key_slice + 2, slice_end) + 1)
+
+        for organ in TARGET_ORGANS:
+            monai_organ = f"MONAI_{organ}"
+
+            for slice_idx in overlap_slices:
+                slice_filename = f"{slice_idx:03}_OUT.png"
+                mask_path = os.path.join(series_path, monai_organ, slice_filename)
+
+                if not os.path.exists(mask_path):
+                    continue
+
+                mask_gray = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+                if mask_gray is None or mask_gray.shape[0] == 0:
+                    continue
+
+                mask_gray = cv2.rotate(mask_gray, cv2.ROTATE_90_COUNTERCLOCKWISE)
+                mask_gray = cv2.flip(mask_gray, 0)
+
+                mask_crop = mask_gray[bbox_y1:bbox_y2, bbox_x1:bbox_x2]
+                overlap_area = np.sum(mask_crop > 0)
+                overlap_ratio = overlap_area / lesion_area
+
+                if overlap_ratio > OVERLAP_THRESHOLD:
+                    matched_organs.add(organ)
+                    found_overlap = True
+                    break
+
+            if not found_overlap:  
+                proximity_distances = []  
+                all_distances = []  
+
+                for organ in TARGET_ORGANS:  
+                    monai_organ = f"MONAI_{organ}"  
+                    slice_filename = f"{key_slice:03}_OUT.png"  
+                    mask_path = os.path.join(series_path, monai_organ, slice_filename)  
+
+                    if not os.path.exists(mask_path):  
+                        continue  
+
+                    mask_gray = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)  
+                    if mask_gray is None or np.sum(mask_gray > 0) == 0:  
+                        continue  
+
+                    mask_gray = cv2.rotate(mask_gray, cv2.ROTATE_90_COUNTERCLOCKWISE)  
+                    mask_gray = cv2.flip(mask_gray, 0)  
+
+                    mask_bin = (mask_gray > 0).astype(np.uint8)  
+                    dist_transform = cv2.distanceTransform(255 - mask_bin * 255, cv2.DIST_L2, 5)  
+
+                    lesion_mask = np.zeros_like(mask_bin)  
+                    lesion_mask[bbox_y1:bbox_y2, bbox_x1:bbox_x2] = 1  
+                    lesion_dist = dist_transform * lesion_mask  
+
+                    if np.any(lesion_dist > 0):  
+                        min_dist = lesion_dist[lesion_dist > 0].min()  
+                        all_distances.append((organ, min_dist))  
+                        
+                        if min_dist <= 10:
+                            matched_organs.add(organ)
+                            found_overlap = True
+                            proximity_distances.clear()
+                            
+
+                        if min_dist >10 and min_dist <= 20 and not found_overlap  : 
+                            proximity_distances.append((organ, min_dist))  
+
+
+                for organ, _ in proximity_distances:  
+                    matched_organs.add(organ + "_prox")  
+                            
+        results.append({
+            "series": series_name,
+            "slice_range": f"{slice_start}~{slice_end}",
+            "key_slice": key_slice,
+            "lesion_id": idx,
+            "matched_organs": ";".join(sorted(matched_organs)) if matched_organs else "none"
+        })
+
+    except Exception as e:
+        print(f"⚠️ error {idx}: {e}")
+        continue
+
+
+out_df = pd.DataFrame(results)
+out_df.to_csv(OUTPUT_PATH, index=False)
+

Phase I/monai_segmentation.py

@@ -0,0 +1,13 @@
+from monai.bundle import ConfigParser
+
+config_path = "monai_wholeBody_ct_segmentation/configs/inference.json"
+
+parser = ConfigParser()
+parser.read_config(config_path)
+
+evaluator = parser.get_parsed_content("evaluator")
+
+try:
+    evaluator.run()
+except Exception as e:
+    print(f"⚠️ خطا در اجرای evaluator: {e}")

Phase I/monai_wholeBody_ct_segmentation/.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

Phase I/monai_wholeBody_ct_segmentation/.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,33 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyInterpreterInspection" enabled="false" level="WARNING" enabled_by_default="false" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="18">
+            <item index="0" class="java.lang.String" itemvalue="scipy" />
+            <item index="1" class="java.lang.String" itemvalue="protobuf" />
+            <item index="2" class="java.lang.String" itemvalue="thop" />
+            <item index="3" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="4" class="java.lang.String" itemvalue="PyYAML" />
+            <item index="5" class="java.lang.String" itemvalue="cython" />
+            <item index="6" class="java.lang.String" itemvalue="ipython" />
+            <item index="7" class="java.lang.String" itemvalue="numpy" />
+            <item index="8" class="java.lang.String" itemvalue="requests" />
+            <item index="9" class="java.lang.String" itemvalue="psutil" />
+            <item index="10" class="java.lang.String" itemvalue="tqdm" />
+            <item index="11" class="java.lang.String" itemvalue="pandas" />
+            <item index="12" class="java.lang.String" itemvalue="tensorboard" />
+            <item index="13" class="java.lang.String" itemvalue="seaborn" />
+            <item index="14" class="java.lang.String" itemvalue="matplotlib" />
+            <item index="15" class="java.lang.String" itemvalue="Pillow" />
+            <item index="16" class="java.lang.String" itemvalue="line-bot-sdk" />
+            <item index="17" class="java.lang.String" itemvalue="monai" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+    <inspection_tool class="VulnerableLibrariesLocal" enabled="false" level="WARNING" enabled_by_default="false" />
+  </profile>
+</component>

Phase I/monai_wholeBody_ct_segmentation/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

Phase I/monai_wholeBody_ct_segmentation/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/monai_wholeBody_ct_segmentation.iml" filepath="$PROJECT_DIR$/.idea/monai_wholeBody_ct_segmentation.iml" />
+    </modules>
+  </component>
+</project>

Phase I/monai_wholeBody_ct_segmentation/.idea/monai_wholeBody_ct_segmentation.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

Phase I/monai_wholeBody_ct_segmentation/.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>