File size: 8,551 Bytes
6f7f115 458f5af 6f7f115 beb6c08 6f7f115 9ea4504 6f7f115 9ea4504 6f7f115 9ea4504 6f7f115 458f5af 6f7f115 9ea4504 6f7f115 9ea4504 6f7f115 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 |
import os
import pandas as pd
import torch
from torch.utils.data import Dataset
from torchvision.io import read_image
# 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']
def mimic_cxr_image_path(dir, subject_id, study_id, dicom_id, ext='dcm'):
return os.path.join(dir, 'p' + str(subject_id)[:2], 'p' + str(subject_id),
's' + str(study_id), str(dicom_id) + '.' + ext)
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
|