Organ-aware 3D lesion segmentation dataset and pipeline for abdominal CT analysis (ACM Multimedia 2025 candidate)
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

metadata.json 7.5KB

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185
  1. {
  2. "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
  3. "version": "0.1.2",
  4. "changelog": {
  5. "0.1.2": "Update figure with links",
  6. "0.1.1": "adapt to BundleWorkflow interface and val metric",
  7. "0.1.0": "complete the model package",
  8. "0.0.1": "initialize the model package structure"
  9. },
  10. "monai_version": "1.2.0rc3",
  11. "pytorch_version": "1.13.1",
  12. "numpy_version": "1.22.2",
  13. "optional_packages_version": {
  14. "nibabel": "4.0.1",
  15. "pytorch-ignite": "0.4.9"
  16. },
  17. "name": "Whole body CT segmentation",
  18. "task": "TotalSegmentator Segmentation",
  19. "description": "A pre-trained SegResNet model for volumetric (3D) segmentation of the 104 whole body segments",
  20. "authors": "MONAI team",
  21. "copyright": "Copyright (c) MONAI Consortium",
  22. "data_source": "TotalSegmentator",
  23. "data_type": "nibabel",
  24. "image_classes": "104 foreground channels, 0 channel for the background, intensity scaled to [0, 1]",
  25. "label_classes": "0 is the background, others are whole body segments",
  26. "pred_classes": "0 is the background, 104 other chanels are whole body segments",
  27. "eval_metrics": {
  28. "mean_dice": 0.8
  29. },
  30. "intended_use": "This is an example, not to be used for diagnostic purposes",
  31. "references": [
  32. "Wasserthal, J., Meyer, M., Breit, H.C., Cyriac, J., Yang, S. and Segeroth, M., 2022. TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868.",
  33. "Myronenko, A., Siddiquee, M.M.R., Yang, D., He, Y. and Xu, D., 2022. Automated head and neck tumor segmentation from 3D PET/CT. arXiv preprint arXiv:2209.10809.",
  34. "Tang, Y., Gao, R., Lee, H.H., Han, S., Chen, Y., Gao, D., Nath, V., Bermudez, C., Savona, M.R., Abramson, R.G. and Bao, S., 2021. High-resolution 3D abdominal segmentation with random patch network fusion. Medical image analysis, 69, p.101894."
  35. ],
  36. "network_data_format": {
  37. "inputs": {
  38. "image": {
  39. "type": "image",
  40. "format": "hounsfield",
  41. "modality": "CT",
  42. "num_channels": 1,
  43. "spatial_shape": [
  44. 96,
  45. 96,
  46. 96
  47. ],
  48. "dtype": "float32",
  49. "value_range": [
  50. 0,
  51. 1
  52. ],
  53. "is_patch_data": true,
  54. "channel_def": {
  55. "0": "image"
  56. }
  57. }
  58. },
  59. "outputs": {
  60. "pred": {
  61. "type": "image",
  62. "format": "segmentation",
  63. "num_channels": 105,
  64. "spatial_shape": [
  65. 96,
  66. 96,
  67. 96
  68. ],
  69. "dtype": "float32",
  70. "value_range": [
  71. 0,
  72. 104
  73. ],
  74. "is_patch_data": true,
  75. "channel_def": {
  76. "0": "background",
  77. "1": "spleen",
  78. "2": "kidney_right",
  79. "3": "kidney_left",
  80. "4": "gallbladder",
  81. "5": "liver",
  82. "6": "stomach",
  83. "7": "aorta",
  84. "8": "inferior_vena_cava",
  85. "9": "portal_vein_and_splenic_vein",
  86. "10": "pancreas",
  87. "11": "adrenal_gland_right",
  88. "12": "adrenal_gland_left",
  89. "13": "lung_upper_lobe_left",
  90. "14": "lung_lower_lobe_left",
  91. "15": "lung_upper_lobe_right",
  92. "16": "lung_middle_lobe_right",
  93. "17": "lung_lower_lobe_right",
  94. "18": "vertebrae_L5",
  95. "19": "vertebrae_L4",
  96. "20": "vertebrae_L3",
  97. "21": "vertebrae_L2",
  98. "22": "vertebrae_L1",
  99. "23": "vertebrae_T12",
  100. "24": "vertebrae_T11",
  101. "25": "vertebrae_T10",
  102. "26": "vertebrae_T9",
  103. "27": "vertebrae_T8",
  104. "28": "vertebrae_T7",
  105. "29": "vertebrae_T6",
  106. "30": "vertebrae_T5",
  107. "31": "vertebrae_T4",
  108. "32": "vertebrae_T3",
  109. "33": "vertebrae_T2",
  110. "34": "vertebrae_T1",
  111. "35": "vertebrae_C7",
  112. "36": "vertebrae_C6",
  113. "37": "vertebrae_C5",
  114. "38": "vertebrae_C4",
  115. "39": "vertebrae_C3",
  116. "40": "vertebrae_C2",
  117. "41": "vertebrae_C1",
  118. "42": "esophagus",
  119. "43": "trachea",
  120. "44": "heart_myocardium",
  121. "45": "heart_atrium_left",
  122. "46": "heart_ventricle_left",
  123. "47": "heart_atrium_right",
  124. "48": "heart_ventricle_right",
  125. "49": "pulmonary_artery",
  126. "50": "brain",
  127. "51": "iliac_artery_left",
  128. "52": "iliac_artery_right",
  129. "53": "iliac_vena_left",
  130. "54": "iliac_vena_right",
  131. "55": "small_bowel",
  132. "56": "duodenum",
  133. "57": "colon",
  134. "58": "rib_left_1",
  135. "59": "rib_left_2",
  136. "60": "rib_left_3",
  137. "61": "rib_left_4",
  138. "62": "rib_left_5",
  139. "63": "rib_left_6",
  140. "64": "rib_left_7",
  141. "65": "rib_left_8",
  142. "66": "rib_left_9",
  143. "67": "rib_left_10",
  144. "68": "rib_left_11",
  145. "69": "rib_left_12",
  146. "70": "rib_right_1",
  147. "71": "rib_right_2",
  148. "72": "rib_right_3",
  149. "73": "rib_right_4",
  150. "74": "rib_right_5",
  151. "75": "rib_right_6",
  152. "76": "rib_right_7",
  153. "77": "rib_right_8",
  154. "78": "rib_right_9",
  155. "79": "rib_right_10",
  156. "80": "rib_right_11",
  157. "81": "rib_right_12",
  158. "82": "humerus_left",
  159. "83": "humerus_right",
  160. "84": "scapula_left",
  161. "85": "scapula_right",
  162. "86": "clavicula_left",
  163. "87": "clavicula_right",
  164. "88": "femur_left",
  165. "89": "femur_right",
  166. "90": "hip_left",
  167. "91": "hip_right",
  168. "92": "sacrum",
  169. "93": "face",
  170. "94": "gluteus_maximus_left",
  171. "95": "gluteus_maximus_right",
  172. "96": "gluteus_medius_left",
  173. "97": "gluteus_medius_right",
  174. "98": "gluteus_minimus_left",
  175. "99": "gluteus_minimus_right",
  176. "100": "autochthon_left",
  177. "101": "autochthon_right",
  178. "102": "iliopsoas_left",
  179. "103": "iliopsoas_right",
  180. "104": "urinary_bladder"
  181. }
  182. }
  183. }
  184. }
  185. }