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import os
import pandas as pd
import torch
from torch.utils.data import Dataset
from torchvision.io import read_image
from tools.utils import mimic_cxr_image_path
# Ordered by oblique, lateral, AP, and then PA views so that PA views are closest in position to the generated tokens (and oblique is furtherest).
VIEW_ORDER = ['LPO', 'RAO', 'LAO', 'SWIMMERS', 'XTABLE LATERAL', 'LL', 'LATERAL', 'AP AXIAL', 'AP RLD', 'AP LLD', 'AP', 'PA RLD', 'PA LLD', 'PA']
class StudyIDEDStayIDSubset(Dataset):
"""
Study ID & ED stay ID subset. Examples are indexed by the study identifier.
Information from the ED module is added by finding the study_id that is within
the timespan of the stay_id for the subject_id. The history and indication
sections are also included.
"""
def __init__(
self,
split,
records,
dataset_dir=None,
max_images_per_study=None,
transforms=None,
images=True,
columns='study_id, dicom_id, subject_id, findings, impression',
and_condition='',
study_id_inclusion_list=None,
return_images=True,
ed_module=True,
extension='jpg',
):
"""
Argument/s:
split - 'train', 'validate', or 'test'.
dataset_dir - Dataset directory.
records - MIMIC-CXR & MIMIC-IV-ED records class instance.
max_images_per_study - the maximum number of images per study.
transforms - torchvision transformations.
colour_space - PIL target colour space.
images - flag to return processed images.
columns - which columns to query on.
and_condition - AND condition to add to the SQL query.
study_id_inclusion_list - studies not in this list are excluded.
return_images - return CXR images for the study as tensors.
ed_module - use the ED module.
extension - 'jpg' or 'dcm'.
"""
super(StudyIDEDStayIDSubset, self).__init__()
self.split = split
self.dataset_dir = dataset_dir
self.records = records
self.max_images_per_study = max_images_per_study
self.transforms = transforms
self.images = images
self.columns = columns
self.and_condition = and_condition
self.return_images = return_images
self.ed_module = ed_module
self.extension = extension
# If max images per study is not set:
self.max_images_per_study = float('inf') if self.max_images_per_study is None else self.max_images_per_study
assert self.extension == 'jpg' or self.extension == 'dcm'
if self.dataset_dir is not None:
if self.extension == 'jpg':
if 'physionet.org/files/mimic-cxr-jpg/2.0.0/files' not in self.dataset_dir:
self.dataset_dir = os.path.join(self.dataset_dir, 'physionet.org/files/mimic-cxr-jpg/2.0.0/files')
elif self.extension == 'dcm':
if 'physionet.org/files/mimic-cxr/2.0.0/files' not in self.dataset_dir:
self.dataset_dir = os.path.join(self.dataset_dir, 'physionet.org/files/mimic-cxr/2.0.0/files')
query = f"""
SELECT {columns}
FROM mimic_cxr
WHERE split = '{split}'
{and_condition}
ORDER BY study_id
"""
# For multi-image, the study identifiers make up the training examples:
df = self.records.connect.sql(query).df()
# Drop studies that don't have a findings or impression section:
df = df.dropna(subset=['findings', 'impression'], how='any')
# This study has two rows in edstays (removed as it causes issues):
if self.ed_module:
df = df[df['study_id'] != 59128861]
# Exclude studies not in list:
if study_id_inclusion_list is not None:
df = df[df['study_id'].isin(study_id_inclusion_list)]
# Example study identifiers for the subset:
self.examples = df['study_id'].unique().tolist()
# Record statistics:
self.num_study_ids = len(self.examples)
self.num_dicom_ids = len(df['dicom_id'].unique().tolist())
self.num_subject_ids = len(df['subject_id'].unique().tolist())
def __len__(self):
return self.num_study_ids
def __getitem__(self, index):
study_id = self.examples[index]
# Get the study:
study = self.records.connect.sql(
f"""
SELECT dicom_id, study_id, subject_id, study_datetime, ViewPosition
FROM mimic_cxr
WHERE (study_id = {study_id});
"""
).df()
subject_id = study.iloc[0, study.columns.get_loc('subject_id')]
study_id = study.iloc[0, study.columns.get_loc('study_id')]
study_datetime = study['study_datetime'].max()
example_dict = {
'study_ids': study_id,
'subject_id': subject_id,
'index': index,
}
example_dict.update(self.records.return_mimic_cxr_features(study_id))
if self.ed_module:
edstays = self.records.connect.sql(
f"""
SELECT stay_id, intime, outtime
FROM edstays
WHERE (subject_id = {subject_id})
AND intime < '{study_datetime}'
AND outtime > '{study_datetime}';
"""
).df()
assert len(edstays) <= 1
stay_id = edstays.iloc[0, edstays.columns.get_loc('stay_id')] if not edstays.empty else None
self.records.clear_start_end_times()
example_dict.update(self.records.return_ed_module_features(stay_id, study_datetime))
example_dict['stay_ids'] = stay_id
if self.return_images:
example_dict['images'], example_dict['image_time_deltas'] = self.get_images(study, study_datetime)
return example_dict
def get_images(self, example, reference_time):
"""
Get the image/s for a given example.
Argument/s:
example - dataframe for the example.
reference_time - reference_time for time delta.
Returns:
The image/s for the example
"""
# Sample if over max_images_per_study. Only allowed during training:
if len(example) > self.max_images_per_study:
assert self.split == 'train'
example = example.sample(n=self.max_images_per_study, axis=0)
# Order by ViewPostion:
example['ViewPosition'] = example['ViewPosition'].astype(pd.CategoricalDtype(categories=VIEW_ORDER, ordered=True))
# Sort the DataFrame based on the categorical column
example = example.sort_values(by=['study_datetime', 'ViewPosition'])
# Load and pre-process each CXR:
images, time_deltas = [], []
for _, row in example.iterrows():
images.append(
self.load_and_preprocess_image(
row['subject_id'],
row['study_id'],
row['dicom_id'],
),
)
time_deltas.append(self.records.compute_time_delta(row['study_datetime'], reference_time, to_tensor=False))
if self.transforms is not None:
images = torch.stack(images, 0)
return images, time_deltas
def load_and_preprocess_image(self, subject_id, study_id, dicom_id):
"""
Load and preprocess an image using torchvision.transforms.v2:
https://pytorch.org/vision/stable/auto_examples/transforms/plot_transforms_getting_started.html#sphx-glr-auto-examples-transforms-plot-transforms-getting-started-py
Argument/s:
subject_id - subject identifier.
study_id - study identifier.
dicom_id - DICOM identifier.
Returns:
image - Tensor of the CXR.
"""
if self.extension == 'jpg':
image_file_path = mimic_cxr_image_path(self.dataset_dir, subject_id, study_id, dicom_id, self.extension)
image = read_image(image_file_path)
elif self.extension == 'dcm':
raise NotImplementedError
if self.transforms is not None:
image = self.transforms(image)
return image
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