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Karrar-Alhdrawi/brain-stroke-ct-dataset

AI-Based Intelligent Extraction of Diseased Areas from Medical Radiography Images 1. Overview This dataset is a refined and preprocessed version of the Brain Stroke CT Dataset, specifically optimized for semantic segmentation tasks using deep learning architectures like U-Net. This work is part of a research project focused on automating the extraction of pathological regions (Bleeding and Ischemia) from head CT scans to assist in rapid clinical diagnosis.… See the full description on the dataset page: https://huggingface.co/datasets/Karrar-Alhdrawi/brain-stroke-ct-dataset.

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AI-Based Intelligent Extraction of Diseased Areas from Medical Radiography Images

1. Overview

This dataset is a refined and preprocessed version of the Brain Stroke CT Dataset, specifically optimized for semantic segmentation tasks using deep learning architectures like U-Net. This work is part of a research project focused on automating the extraction of pathological regions (Bleeding and Ischemia) from head CT scans to assist in rapid clinical diagnosis.

2. Source & Attribution

The raw data was originally sourced from the Brain Stroke CT Dataset on Kaggle, which was prepared by radiologists and supported by the Turkish Health Institutes (TUSEB).

3. Preprocessing & Methodology

The original dataset provided raw CT images and "Overlay" images where specialists manually highlighted diseased areas using specific colors (Red and Green). To make this data compatible with high-precision segmentation models, a custom Python pipeline was developed to:

—Binary Mask Extraction: Used HSV color masking to isolate the clinicians' annotations from the overlays, converting them into pure binary masks (White: Diseased, Black: Healthy).
—Normal Case Synthesis: Generated corresponding empty (all-black) masks for healthy brain images to maintain consistency during model training.
—Structural Standardization: Organized the data into a clean images/ and masks/ hierarchy for each category (Normal, Bleeding, Ischemia).

4. Why This Custom Pipeline?

The raw Kaggle dataset is excellent for classification, but lacks ready-to-use binary masks for pixel-wise segmentation. By applying this custom script:

1.We standardized the labels for U-Net ingestion.
2.We ensured that the model learns the exact morphology of the stroke rather than just its general location.
3.We reduced data noise by filtering out non-essential metadata and raw DICOM formats, focusing on standardized PNG outputs.

5. Implementation Script

The following script was used to generate this processed version:

python

# [
import os
import shutil
import cv2
import numpy as np
from tqdm import tqdm
import kagglehub
from huggingface_hub import HfApi, create_repo

# --- إعدادات الحساب والروابط ---
my_token = "***********"
repo_name = "iraqigold/brain-stroke-ct-dataset"

# مسارات الشغل بداخل الكولاب
raw_path = "/content/brain-stroke-ct-dataset"
output_folder = "/content/brain-stroke"
zip_name = "/content/brain-stroke-dataset-v1" 

# --- تنظيف المكان وتحميل الداتا ---
print("جاري تنظيف المجلدات وتحميل البيانات من كاجل...")
if os.path.exists(raw_path): shutil.rmtree(raw_path)
if os.path.exists(output_folder): shutil.rmtree(output_folder)

# تحميل الداتا سيت
tmp_path = kagglehub.dataset_download("ozguraslank/brain-stroke-ct-dataset")
shutil.copytree(tmp_path, raw_path)

# التأكد من المسار الصحيح للمجلد بداخل الداتا سيت
data_root = raw_path
if "Brain_Stroke_CT_Dataset" in os.listdir(raw_path):
    data_root = os.path.join(raw_path, "Brain_Stroke_CT_Dataset")

# --- دالة استخراج الماسك من الألوان (أحمر وأخضر) ---
def get_mask(path):
    # نقرأ الصورة ونحولها لنمط HSV حتى نعزل الألوان
    image = cv2.imread(path)
    hsv_img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    
    # تحديد درجات اللون الأحمر والأخضر
    r1_low, r1_high = np.array([0, 70, 50]), np.array([10, 255, 255])
    r2_low, r2_high = np.array([170, 70, 50]), np.array([180, 255, 255])
    g_low, g_high = np.array([35, 50, 50]), np.array([85, 255, 255])
    
    # نسوي ماسك لكل لون ونجمعهم
    m1 = cv2.inRange(hsv_img, r1_low, r1_high)
    m2 = cv2.inRange(hsv_img, r2_low, r2_high)
    m3 = cv2.inRange(hsv_img, g_low, g_high)
    
    final_mask = cv2.bitwise_or(cv2.bitwise_or(m1, m2), m3)
    # نحولها لأسود وأبيض صافي (Binary)
    _, thresh = cv2.threshold(final_mask, 1, 255, cv2.THRESH_BINARY)
    return thresh

# --- ترتيب الفولدرات ومعالجة الصور ---
print("بدأنا ترتيب الفولدرات واستخراج الماسكات...")
folders = ["Bleeding", "Ischemia", "Normal"]

for f in folders:
    os.makedirs(os.path.join(output_folder, f, "images"), exist_ok=True)
    os.makedirs(os.path.join(output_folder, f, "masks"), exist_ok=True)

# نمر على كل نوع مرض ونعالج صوره
for folder in folders:
    print(f"جاري معالجة نوع: {folder}")
    path_now = os.path.join(data_root, folder)
    
    png_folder = os.path.join(path_now, "PNG")
    overlay_folder = os.path.join(path_now, "OVERLAY")
    
    if not os.path.exists(png_folder): continue

    for img_name in tqdm(os.listdir(png_folder)):
        if not img_name.lower().endswith(('.png', '.jpg', '.jpeg')): continue
        
        # نقل الصورة الأصلية لمكانها الجديد
        old_img = os.path.join(png_folder, img_name)
        new_img = os.path.join(output_folder, folder, "images", img_name)
        shutil.copy(old_img, new_img)
        
        # نحدد مكان حفظ الماسك الجديد
        new_mask = os.path.join(output_folder, folder, "masks", img_name)
        
        if folder == "Normal":
            # إذا الحالة سليمة، نسوي ماسك أسود فارغ بنفس الحجم
            temp_img = cv2.imread(old_img)
            empty_mask = np.zeros((temp_img.shape[0], temp_img.shape[1]), dtype=np.uint8)
            cv2.imwrite(new_mask, empty_mask)
        else:
            # إذا اكو مرض، نستخرج الماسك من صور الـ Overlay الملونة
            ov_path = os.path.join(overlay_folder, img_name)
            if os.path.exists(ov_path):
                binary_res = get_mask(ov_path)
                cv2.imwrite(new_mask, binary_res)

# --- ضغط البيانات ورفعها ---
print("جاري ضغط الملفات...")
shutil.make_archive(zip_name, 'zip', output_folder)
size = os.path.getsize(f"{zip_name}.zip") / (1024*1024)
print(f"حجم الملف المضغوط: {size:.2f} MB")

print("بدء عملية الرفع إلى Hugging Face...")
hf_api = HfApi()
create_repo(repo_name, token=my_token, repo_type="dataset", exist_ok=True)

# رفع المجلد مفتوح للمعاينة
hf_api.upload_folder(
    folder_path=output_folder,
    repo_id=repo_name,
    token=my_token,
    repo_type="dataset"
)

# رفع ملف الـ Zip للتحميل السريع
hf_api.upload_file(
    path_or_fileobj=f"{zip_name}.zip",
    path_in_repo="brain-stroke-dataset-v1.zip",
    repo_id=repo_name,
    token=my_token,
    repo_type="dataset"
)

print(f"تم بنجاح! الرابط: https://huggingface.co/datasets/{repo_name}")
]