soorena62
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
from dagster import job, graph, op
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from pathlib import Path
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from .ops import (
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read_image_and_mask,
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read_image_and_mask_from_case,
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filter_image,
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extract_features,
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write_outputs,
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enumerate_cases_auto
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)
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# Extract image file name from config for single file mode
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@op(config_schema={"image_path": str})
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def get_case_name_from_config(context) -> str:
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image_path = context.op_config["image_path"]
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return Path(image_path).stem
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# Extract case_name from case dictionary for batch mode
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@op
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def get_case_name_from_case(context, case: dict) -> str:
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return case["case_name"]
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# Single-case workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def radiomics_job():
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image, mask = read_image_and_mask()
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name)
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# Graph that processes one case dict (used for batch mapping)
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@graph
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def process_case(case):
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image, mask = read_image_and_mask_from_case(case)
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_case(case)
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write_outputs(features=feats, case_name=case_name)
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# Batch workflow job (auto-discovery + dynamic mapping)
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@job
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def radiomics_batch_job():
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cases = enumerate_cases_auto()
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cases.map(process_case)
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# Preprocessing-only workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def preprocessing_job():
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image, mask = read_image_and_mask()
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filtered = filter_image(image)
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feats = extract_features(image=filtered, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name)
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# Feature-extraction-only workflow job
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@job(config={
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"ops": {
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"read_image_and_mask": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz",
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"mask_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/masks/CT_pitch_mask.nii.gz",
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}
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},
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"get_case_name_from_config": {
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"config": {
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"image_path": "C:/Users/Omen16/Documents/Radiuma_Mini/data/images/CT_pitch.nii.gz"
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}
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}
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}
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})
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def feature_extraction_job():
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image, mask = read_image_and_mask()
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feats = extract_features(image=image, mask=mask)
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case_name = get_case_name_from_config()
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write_outputs(features=feats, case_name=case_name) |