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split lymph node data set; data set referenes

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docs/results/nnDetectionV001.md
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......@@ -49,20 +49,20 @@ ADAM Results are listed under Benchmarks
5 Fold Cross Validation
| Model | Lymph Nodes |
|:-----------:|:-----------:|
| nnDetection | 0.205 |
| nnUNetPlus | 0.162 |
| nnUNetBasic | 0.159 |
| Model | Abdominal Lymph Nodes | Mediastinal Lymph Nodes |
|:-----------:|:---------------------:|:-----------------------:|
| nnDetection | 0.493 | 0.440 |
| nnUNetPlus | 0.378 | 0.334 |
| nnUNetBasic | 0.360 | 0.302 |
 
Test Split
| Model | Lymph Nodes |
|:-----------:|:-----------:|
| nnDetection | 0.270 |
| nnUNetPlus | 0.169 |
| Model | Abdominal Lymph Nodes | Mediastinal Lymph Nodes |
|:-----------:|:---------------------:|:-----------------------:|
| nnDetection | 0.470 | 0.500 |
| nnUNetPlus | 0.311 | 0.342 |
Luna results are listed under Benchmarks
......@@ -70,6 +70,18 @@ Luna results are listed under Benchmarks
</div>
#### References
- S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P.Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al. The lungimage database consortium (lidc) and image database resource initiative (idri):a completed reference database of lung nodules on ct scans.Medical physics,38(2):915–931, 2011
- L. Jin, J. Yang, K. Kuang, B. Ni, Y. Gao, Y. Sun, P. Gao, W. Ma, M. Tan, H. Kang,J. Chen, and M. Li. Deep-learning-assisted detection and segmentation of ribfractures from CT scans: Development and validation of FracNet. 62. Publisher:Elsevier
- C. Tabea Kossen, L. Kaufhold, M. H ̈ullebrand, J.-M. Kuhnigk, J. Br ̈uhning,J. Schaller, B. Pfahringer, A. Spuler, L. Goubergrits, and A. Hennemuth. Cerebralaneurysm detection and analysis, Mar. 2020
- K. Timmins, E. Bennink, I. van der Schaaf, B. Velthuis, Y. Ruigrok, and H. Kuijf.Intracranial Aneurysm Detection and Segmentation Challenge, Mar. 2020.
- N. Heller, N. Sathianathen, A. Kalapara, E. Walczak, K. Moore, H. Kaluzniak,J. Rosenberg, P. Blake, Z. Rengel, M. Oestreich, et al. The kits19 challenge data:300 kidney tumor cases with clinical context, ct semantic segmentations, and sur-gical outcomes.arXiv preprint arXiv:1904.00445, 2019
- G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman. Computer-aided detection of prostate cancer in mri.IEEE TMI, 33(5):1083–1092, 2014
- R. Cuocolo, A. Comelli, A. Stefano, V. Benfante, N. Dahiya, A. Stanzione,A. Castaldo, D. R. D. Lucia, A. Yezzi, and M. Imbriaco. Deep learning whole-gland and zonal prostate segmentation on a public mri dataset.Journal of Mag-netic Resonance Imaging, 2021.
- A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. Van Ginneken,A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, et al. A large anno-tated medical image dataset for the development and evaluation of segmentationalgorithms.arXiv preprint arXiv:1902.09063, 2019.
- H. R. Roth, L. Lu, A. Seff, K. M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey,and R. M. Summers. A new 2.5 d representation for lymph node detection usingrandom sets of deep convolutional neural network observations. InMICCAI, pages520–527. Springer, 2014
- A. Seff, L. Lu, A. Barbu, H. Roth, H.-C. Shin, and R. M. Summers. Leveraging mid-level semantic boundary cues for automated lymph node detection. InMICCAI,pages 53–61. Springer, 2015
## Benchmarks
### Luna
Disclaimer:
......@@ -87,7 +99,6 @@ Zhu et al. (2018) | 0.692 | 0.769 | 0.824 | 0.865 | 0.893 | 0.917 |
Wang et al. (2018) | 0.676 | 0.776 | 0.879 | 0.949 | 0.958 | 0.958 | 0.958 | 0.878 |
Ding et al. (2017) | 0.748 | 0.853 | 0.887 | 0.922 | 0.938 | 0.944 | 0.946 | 0.891 |
Khosravan et al. (2018) | 0.709 | 0.836 | 0.921 | 0.953 | 0.953 | 0.953 | 0.953 | 0.897 |
Cao et al. (2020) | 0.868 | 0.900 | 0.913 | 0.915 | 0.916 | 0.931 | 0.932 | 0.911 |
Liu et al. (2019) | 0.848 | 0.876 | 0.905 | 0.933 | 0.943 | 0.957 | 0.970 | 0.919 |
Song et al. (2020) | 0.723 | 0.838 | 0.887 | 0.911 | 0.928 | 0.934 | 0.948 | 0.881 |
nnDetection v0.1 (ours, 2021) | 0.812 | 0.885 | 0.927 | 0.950 | 0.969 | 0.979 | 0.985 | 0.930 |
......@@ -96,12 +107,15 @@ Cao et al. (2020) + FPR | 0.848 | 0.899 | 0.925 | 0.936 | 0.949 | 0.957 |
Liu et al. (2019) + FPR | 0.904 | 0.914 | 0.933 | 0.957 | 0.971 | 0.971 | 0.971 | 0.952 |
<sup>*</sup> Some of the other methods also use FPR stages but the methods listed below report results w. and wo. FPR.
&nbsp;
</div>
#### References (no particular oder)
- A. A. A. Setio, A. Traverso, T. de Bel, M. S. Berens, C. van den Bogaard, P. Cerello,H. Chen, Q. Dou, M. E. Fantacci, B. Geurts, R. van der Gugten, P. A. Heng,B. Jansen, M. M. de Kaste, V. Kotov, J. Y.-H. Lin, J. T. Manders, A. S ́o ̃nora-Mengana, J. C. Garc ́ıa-Naranjo, E. Papavasileiou, M. Prokop, M. Saletta, C. M.Schaefer-Prokop, E. T. Scholten, L. Scholten, M. M. Snoeren, E. L. Torres, J. Van-demeulebroucke, N. Walasek, G. C. Zuidhof, B. van Ginneken, and C. Jacobs.Validation, comparison, and combination of algorithms for automatic detection ofpulmonary nodules in computed tomography images: The luna16 challenge.Me-dIA, 42:1–13, 2017.
- Z. Gong, D. Li, J. Lin, Y. Zhang and K. -M. Lam, "Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network," in IEEE Access, vol. 8, pp. 157391-157402, 2020, doi: 10.1109/ACCESS.2020.3019104
- Q. Dou, H. Chen, L. Yu, J. Qin and P. Heng, "Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1558-1567, July 2017, doi: 10.1109/TBME.2016.2613502.
- Gupta, A., Saar, T., Martens, O. and Moullec, Y.L. (2018), Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Med. Phys., 45: 1135-1149. https://doi.org/10.1002/mp.12746
- J. Ding, A. Li, Z. Hu, and L. Wang. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In MICCAI, pages 559–567. Springer, 2017
- Q. Dou, H. Chen, Y. Jin, H. Lin, J. Qin, and P.-A. Heng. Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. In MICCAI, pages 630–638. Springer, 2017
- N. Khosravan and U. Bagci. S4nd: Single-shot single-scale lung nodule detection. In MICCAI, pages 794–802. Springer, 2018.
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projects/Task025_LymphNodes/scripts/prepare.py deleted 100644 → 0
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import os
import shutil
import sys
from itertools import repeat
from multiprocessing import Pool
from pathlib import Path
from nndet.utils.check import env_guard
import numpy as np
from loguru import logger
import SimpleITK as sitk
from nndet.io import save_json
from nndet.io.prepare import create_test_split
from nndet.io.itk import load_sitk_as_array
from nndet.utils.info import maybe_verbose_iterable
def prepare_image(
case_id: str,
base_dir: Path,
mask_dir: Path,
raw_splitted_dir: Path,
):
logger.info(f"Processing {case_id}")
root_data_dir = base_dir / case_id
patient_data_dir = []
for root, dirs, files in os.walk(root_data_dir, topdown=False):
if any([f.endswith(".dcm") for f in files]):
patient_data_dir.append(Path(root))
assert len(patient_data_dir) == 1
patient_data_dir = patient_data_dir[0]
reader = sitk.ImageSeriesReader()
dicom_names = reader.GetGDCMSeriesFileNames(str(patient_data_dir))
reader.SetFileNames(dicom_names)
data_itk = reader.Execute()
patient_label_dir = mask_dir / case_id
label_path = [p for p in patient_label_dir.iterdir() if p.is_file() and p.name.endswith(".nii.gz")]
assert len(label_path) == 1
label_path = label_path[0]
mask = load_sitk_as_array(label_path)[0]
instances = np.unique(mask)
instances = instances[instances > 0]
meta = {"instances": {str(int(i)): 0 for i in instances}}
meta["original_path_data"] = str(patient_data_dir)
meta["original_path_label"] = str(label_path)
save_json(meta, raw_splitted_dir / "labelsTr" / f"{case_id}.json")
sitk.WriteImage(data_itk, str(raw_splitted_dir / "imagesTr" / f"{case_id}_0000.nii.gz"))
shutil.copy(label_path, raw_splitted_dir / "labelsTr" / f"{case_id}.nii.gz")
@env_guard
def main():
det_data_dir = Path(os.getenv("det_data"))
task_data_dir = det_data_dir / "Task025_LymphNodes"
source_data_base = task_data_dir / "raw"
if not source_data_base.is_dir():
raise RuntimeError(f"{source_data_base} should contain the raw data but does not exist.")
raw_splitted_dir = task_data_dir / "raw_splitted"
(raw_splitted_dir / "imagesTr").mkdir(parents=True, exist_ok=True)
(raw_splitted_dir / "labelsTr").mkdir(parents=True, exist_ok=True)
(raw_splitted_dir / "imagesTs").mkdir(parents=True, exist_ok=True)
(raw_splitted_dir / "labelsTs").mkdir(parents=True, exist_ok=True)
logger.remove()
logger.add(sys.stdout, format="{level} {message}", level="DEBUG")
logger.add(raw_splitted_dir.parent / "prepare.log", level="DEBUG")
meta = {
"name": "Lymph Node TCIA",
"task": "Task025_LymphNodes",
"target_class": None,
"test_labels": True,
"labels": {
"0": "LymphNode",
},
"modalities": {
"0": "CT",
},
"dim": 3,
}
save_json(meta, raw_splitted_dir.parent / "dataset.json")
base_dir = source_data_base / "CT Lymph Nodes"
mask_dir = source_data_base / "MED_ABD_LYMPH_MASKS"
case_ids = sorted([p.name for p in base_dir.iterdir() if p.is_dir()])
logger.info(f"Found {len(case_ids)} cases in {base_dir}")
for cid in maybe_verbose_iterable(case_ids):
prepare_image(
case_id=cid,
base_dir=base_dir,
mask_dir=mask_dir,
raw_splitted_dir=raw_splitted_dir,
)
# with Pool(processes=6) as p:
# p.starmap(
# prepare_image,
# zip(
# case_ids,
# repeat(base_dir),
# repeat(mask_dir),
# repeat(raw_splitted_dir)
# )
# )
create_test_split(raw_splitted_dir,
num_modalities=len(meta["modalities"]),
test_size=0.3,
random_state=0,
shuffle=True,
)
if __name__ == '__main__':
main()
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