split lymph node data set; data set referenes

docs/results/nnDetectionV001.md

@@ -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.

projects/Task025_LymphNodes/scripts/prepare.py

-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()