Change split from all to train

README.md

@@ -17,163 +17,163 @@ size_categories:
 configs:
   - config_name: 0001_visceral_gc
     data_files:
-      - split: all
+      - split: train
         path: "0001_visceral_gc/0001_visceral_gc.csv"
   - config_name: 0002_visceral_sc
     data_files:
-      - split: all
+      - split: train
         path: "0002_visceral_sc/0002_visceral_sc.csv"
   - config_name: 0003_kits21
     data_files:
-      - split: all
+      - split: train
         path: "0003_kits21/0003_kits21.csv"
   - config_name: 0004_lits
     data_files:
-      - split: all
+      - split: train
         path: "0004_lits/0004_lits.csv"
   - config_name: 0005_bcv_abdomen
     data_files:
-      - split: all
+      - split: train
         path: "0005_bcv_abdomen/0005_bcv_abdomen.csv"
   - config_name: 0006_bcv_cervix
     data_files:
-      - split: all
+      - split: train
         path: "0006_bcv_cervix/0006_bcv_cervix.csv"
   - config_name: 0007_chaos
     data_files:
-      - split: all
+      - split: train
         path: "0007_chaos/0007_chaos.csv"
   - config_name: 0008_ctorg
     data_files:
-      - split: all
+      - split: train
         path: "0008_ctorg/0008_ctorg.csv"
   - config_name: 0009_abdomenct1k
     data_files:
-      - split: all
+      - split: train
         path: "0009_abdomenct1k/0009_abdomenct1k.csv"
   - config_name: 0010_verse
     data_files:
-      - split: all
+      - split: train
         path: "0010_verse/0010_verse.csv"
   - config_name: 0011_exact
     data_files:
-      - split: all
+      - split: train
         path: "0011_exact/0011_exact.csv"
   - config_name: 0012_cad_pe
     data_files:
-      - split: all
+      - split: train
         path: "0012_cad_pe/0012_cad_pe.csv"
   - config_name: 0013_ribfrac
     data_files:
-      - split: all
+      - split: train
         path: "0013_ribfrac/0013_ribfrac.csv"
   - config_name: 0014_learn2reg
     data_files:
-      - split: all
+      - split: train
         path: "0014_learn2reg/0014_learn2reg.csv"
   - config_name: 0015_lndb
     data_files:
-      - split: all
+      - split: train
         path: "0015_lndb/0015_lndb.csv"
   - config_name: 0016_lidc
     data_files:
-      - split: all
+      - split: train
         path: "0016_lidc/0016_lidc.csv"
   - config_name: 0017_lola11
     data_files:
-      - split: all
+      - split: train
         path: "0017_lola11/0017_lola11.csv"
   - config_name: 0018_sliver07
     data_files:
-      - split: all
+      - split: train
         path: "0018_sliver07/0018_sliver07.csv"
   - config_name: 0019_tcia_ct_lymph_nodes
     data_files:
-      - split: all
+      - split: train
         path: "0019_tcia_ct_lymph_nodes/0019_tcia_ct_lymph_nodes.csv"
   - config_name: 0020_tcia_cptac_ccrcc
     data_files:
-      - split: all
+      - split: train
         path: "0020_tcia_cptac_ccrcc/0020_tcia_cptac_ccrcc.csv"
   - config_name: 0021_tcia_cptac_luad
     data_files:
-      - split: all
+      - split: train
         path: "0021_tcia_cptac_luad/0021_tcia_cptac_luad.csv"
   - config_name: 0022_tcia_ct_images_covid19
     data_files:
-      - split: all
+      - split: train
         path: "0022_tcia_ct_images_covid19/0022_tcia_ct_images_covid19.csv"
   - config_name: 0023_tcia_nsclc_radiomics
     data_files:
-      - split: all
+      - split: train
         path: "0023_tcia_nsclc_radiomics/0023_tcia_nsclc_radiomics.csv"
   - config_name: 0024_pancreas_ct
     data_files:
-      - split: all
+      - split: train
         path: "0024_pancreas_ct/0024_pancreas_ct.csv"
   - config_name: 0025_pancreatic_ct_cbct_seg
     data_files:
-      - split: all
+      - split: train
         path: "0025_pancreatic_ct_cbct_seg/0025_pancreatic_ct_cbct_seg.csv"
   - config_name: 0026_rider_lung_ct
     data_files:
-      - split: all
+      - split: train
         path: "0026_rider_lung_ct/0026_rider_lung_ct.csv"
   - config_name: 0027_tcia_tcga_kich
     data_files:
-      - split: all
+      - split: train
         path: "0027_tcia_tcga_kich/0027_tcia_tcga_kich.csv"
   - config_name: 0028_tcia_tcga_kirc
     data_files:
-      - split: all
+      - split: train
         path: "0028_tcia_tcga_kirc/0028_tcia_tcga_kirc.csv"
   - config_name: 0029_tcia_tcga_kirp
     data_files:
-      - split: all
+      - split: train
         path: "0029_tcia_tcga_kirp/0029_tcia_tcga_kirp.csv"
   - config_name: 0030_tcia_tcga_lihc
     data_files:
-      - split: all
+      - split: train
         path: "0030_tcia_tcga_lihc/0030_tcia_tcga_lihc.csv"
   - config_name: 0032_stoic2021
     data_files:
-      - split: all
+      - split: train
         path: "0032_stoic2021/0032_stoic2021.csv"
   - config_name: 0033_tcia_nlst
     data_files:
-      - split: all
+      - split: train
         path: "0033_tcia_nlst/0033_tcia_nlst.csv"
   - config_name: 0034_empire
     data_files:
-      - split: all
+      - split: train
         path: "0034_empire/0034_empire.csv"
   - config_name: 0037_totalsegmentator
     data_files:
-      - split: all
+      - split: train
         path: "0037_totalsegmentator/0037_totalsegmentator.csv"
   - config_name: 0038_amos
     data_files:
-      - split: all
+      - split: train
         path: "0038_amos/0038_amos.csv"
   - config_name: 0039_han_seg
     data_files:
-      - split: all
+      - split: train
         path: "0039_han_seg/0039_han_seg.csv"
   - config_name: 0040_saros
     data_files:
-      - split: all
+      - split: train
         path: "0040_saros/0040_saros.csv"
   - config_name: 0041_ctrate
     data_files:
-      - split: all
+      - split: train
         path: "0041_ctrate/0041_ctrate.csv"
   - config_name: 0042_new_brainct_1mm
     data_files:
-      - split: all
+      - split: train
         path: "0042_new_brainct_1mm/0042_new_brainct_1mm.csv"
   - config_name: 0043_new_ct_tri
     data_files:
-      - split: all
+      - split: train
         path: "0043_new_ct_tri/0043_new_ct_tri.csv"
 ---
 
@@ -195,9 +195,9 @@ The framework consists of two main components:
 
 2. **CADS-model**: 
    - An open-source model suite for automated whole-body segmentation.
-   - Performance validated on both public challenges and real-world hospital cohorts.
+   - Performance validated on both public chtrainenges and real-world hospital cohorts.
    - Available as Python script run (this GitHub repo) for flexible command-line usage.
-   - Also available as a user-friendly 3D Slicer plugin with UI interface, simple installation and one-click inference.
+   - Also available as a user-friendly 3D Slicer plugin with UI interface, simple insttraination and one-click inference.
 
 <div style="background-color:#fffae6; padding:10px; border-radius:5px;">
 This repository hosts the <strong>CADS-dataset</strong>, providing both original <strong>CT images</strong> and corresponding <strong>segmentation masks</strong> in their native spacing formats. 
@@ -220,7 +220,7 @@ For more information on the dataset (data collection, labeling procedures, and m
 
 ## Format
 
-All images and segmentations are provided in NIfTI format, organized by data source.
+train images and segmentations are provided in NIfTI format, organized by data source.
 
 The directory structure is as follows:
 ```plaintext
@@ -238,23 +238,23 @@ root/
 ## Dataset Sources Overview
 The CADS-dataset comprises multiple publicly available and private-source datasets, each released under its own license.
 
-The table below summarizes all included sources:
+The table below summarizes train included sources:
 
 | Directory Name | Dataset Name | License | Number of CT Volumes | Details |
 |---|---|---|---|---|
 | 0001_visceral_gc | VISCERAL Gold Corpus | Customized license | 40 | [readme](./0001_visceral_gc/README_0001_visceral_gc.md) |
 | 0002_visceral_sc | VISCERAL Silver Corpus | Customized license | 127 | [readme](./0002_visceral_sc/README_0002_visceral_sc.md) |
-| 0003_kits21 | The Kidney and Kidney Tumor Segmentation Challenge （KiTS21） | CC BY-NC-SA 4.0 | 300 | [readme](./0003_kits21/README_0003_kits21.md) |
+| 0003_kits21 | The Kidney and Kidney Tumor Segmentation Chtrainenge （KiTS21） | CC BY-NC-SA 4.0 | 300 | [readme](./0003_kits21/README_0003_kits21.md) |
 | 0004_lits | Liver Tumor Segmentation Benchmark (LiTS) | CC BY-NC-SA 4.0 | 201 | [readme](./0004_lits/README_0004_lits.md) |
 | 0005_bcv_abdomen | MICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Abdomen) | CC BY 4.0 | 50 | [readme](./0005_bcv_abdomen/README_0005_bcv_abdomen.md) |
 | 0006_bcv_cervix | MICCAI Multi-Atlas Labeling Beyond the Cranial Vault (Cervix) | CC BY 4.0 | 50 | [readme](./0006_bcv_cervix/README_0006_bcv_cervix.md) |
-| 0007_chaos | CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge (CT Subset) | CC BY-NC-SA 4.0 | 40 | [readme](./0007_chaos/README_0007_chaos.md) |
+| 0007_chaos | CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation Chtrainenge (CT Subset) | CC BY-NC-SA 4.0 | 40 | [readme](./0007_chaos/README_0007_chaos.md) |
 | 0008_ctorg | CT-ORG: Multiple Organ Segmentation in CT | CC BY 3.0 | 140 | [readme](./0008_ctorg/README_0008_ctorg.md) |
 | 0009_abdomenct1k | AbdomenCT-1K | CC BY 4.0 | 1062 | [readme](./0009_abdomenct1k/README_0009_abdomenct1k.md) |
 | 0010_verse | VerSe – Vertebrae Labelling and Segmentation Benchmark | CC BY-SA 4.0 | 374 | [readme](./0010_verse/README_0010_verse.md) |
 | 0011_exact | EXACT'09 – Extraction of Airways from CT | Customized license | 40 | [readme](./0011_exact/README_0011_exact.md) |
-| 0012_cad_pe | CAD-PE – Computer Aided Detection for Pulmonary Embolism Challenge | CC BY 4.0 | 40 | [readme](./0012_cad_pe/README_0012_cad_pe.md) |
-| 0013_ribfrac | RibFrac Challenge Dataset | CC BY-NC 4.0 | 660 | [readme](./0013_ribfrac/README_0013_ribfrac.md) |
+| 0012_cad_pe | CAD-PE – Computer Aided Detection for Pulmonary Embolism Chtrainenge | CC BY 4.0 | 40 | [readme](./0012_cad_pe/README_0012_cad_pe.md) |
+| 0013_ribfrac | RibFrac Chtrainenge Dataset | CC BY-NC 4.0 | 660 | [readme](./0013_ribfrac/README_0013_ribfrac.md) |
 | 0014_learn2reg | Learn2Reg – Abdomen MR-CT (TCIA Subset) | CC BY 3.0 and TCIA Data Usage Policy | 16 | [readme](./0014_learn2reg/README_0014_learn2reg.md) |
 | 0015_lndb | LNDb – Lung Nodule Database | CC BY-NC-ND 4.0 | 294 | [readme](./0015_lndb/README_0015_lndb.md) |
 | 0016_lidc | LIDC-IDRI – Lung Image Database Consortium and Image Database Resource Initiative | CC BY 3.0 | 997 | [readme](./0016_lidc/README_0016_lidc.md) |
@@ -274,9 +274,9 @@ The table below summarizes all included sources:
 | 0030_tcia_tcga_lihc | TCGA-LIHC (Liver Hepatocellular Carcinoma) | CC BY 3.0 | 242 | [readme](./0030_tcia_tcga_lihc/README_0030_tcia_tcga_lihc.md) |
 | 0032_stoic2021 | STOIC (Study of Thoracic CT in COVID-19) | CC BY-NC 4.0 | 2000 | [readme](./0032_stoic2021/README_0032_stoic2021.md) |
 | 0033_tcia_nlst | National Lung Screening Trial (NLST) | CC BY 4.0 | 7172 | [readme](./0033_tcia_nlst/README_0033_tcia_nlst.md) |
-| 0034_empire | EMPIRE10 Challenge | Customized license | 60 | [readme](./0034_empire/README_0034_empire.md) |
+| 0034_empire | EMPIRE10 Chtrainenge | Customized license | 60 | [readme](./0034_empire/README_0034_empire.md) |
 | 0037_totalsegmentator | TotalSegmentator | CC BY 4.0 | 1203 | [readme](./0037_totalsegmentator/README_0037_totalsegmentator.md) |
-| 0038_amos | AMOS (Multi-Modality Abdominal Multi-Organ Segmentation Challenge) | CC BY 4.0 | 200 | [readme](./0038_amos/README_0038_amos.md) |
+| 0038_amos | AMOS (Multi-Modality Abdominal Multi-Organ Segmentation Chtrainenge) | CC BY 4.0 | 200 | [readme](./0038_amos/README_0038_amos.md) |
 | 0039_han_seg | HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset | CC BY-NC-ND 4.0 | 42 | [readme](./0039_han_seg/README_0039_han_seg.md) |
 | 0040_saros | SAROS: A dataset for whole-body region and organ segmentation in CT imaging | Mix of CC BY 3.0, CC BY 4.0, and CC BY-NC 3.0 | 900 | [readme](./0040_saros/README_0040_saros.md) |
 | 0041_ctrate | CT-RATE | CC BY-NC-SA 4.0 | 3134 | [readme](./0041_ctrate/README_0041_ctrate.md) |