Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring Article Swipe
Jayaram K. Udupa
,
Tiange Liu
,
Chao Jin
,
Liming Zhao
,
Dewey Odhner
,
Yubing Tong
,
Vibhu Agrawal
,
Gargi Pednekar
,
Sanghita Nag
,
Tarun Kotia
,
Michael Goodman
,
E. Paul Wileyto
,
D Mihailidis
,
John N. Lukens
,
Abigail T. Berman
,
Joann Stambaugh
,
Tristan Lim
,
Rupa Chowdary
,
Dheeraj Jalluri
,
Salma K. Jabbour
,
Sung Kim
,
Meral Reyhan
,
Clifford G. Robinson
,
Wade L. Thorstad
,
Jehee Isabelle Choi
,
Robert H. Press
,
Charles B. Simone
,
Joe Camaratta
,
Steve Owens
,
Drew A. Torigian
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/mp.15854
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1002/mp.15854
Background Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end‐to‐end deep learning (DL) networks, are weak in garnering high‐level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge. Purpose We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation. Methods The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI‐based automatic anatomy recognition object recognition (AAR‐R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL‐based recognition (DL‐R), which refines the coarse recognition results of AAR‐R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR‐R fuzzy model of each object guided by the BBs output by DL‐R; and (v) DL‐based delineation (DL‐D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system. Results The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground‐truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53 mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto‐contours and clinically drawn contours. Conclusions The HI system is observed to behave like an expert human in robustness in the contouring task but vastly more efficiently. It seems to use NI help where image information alone will not suffice to decide, first for the correct localization of the object and then for the precise delineation of the boundary.
Related Topics
Concepts
Artificial intelligence
Segmentation
Computer science
Contouring
Robustness (evolution)
Computer vision
Deep learning
Cognitive neuroscience of visual object recognition
Pattern recognition (psychology)
Dice
Medical imaging
Image segmentation
Object (grammar)
Biology
Mathematics
Computer graphics (images)
Geometry
Biochemistry
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/mp.15854
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.15854
- OA Status
- bronze
- Cited By
- 27
- References
- 99
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4285390037
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4285390037Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/mp.15854Digital Object Identifier
- Title
-
Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-14Full publication date if available
- Authors
-
Jayaram K. Udupa, Tiange Liu, Chao Jin, Liming Zhao, Dewey Odhner, Yubing Tong, Vibhu Agrawal, Gargi Pednekar, Sanghita Nag, Tarun Kotia, Michael Goodman, E. Paul Wileyto, D Mihailidis, John N. Lukens, Abigail T. Berman, Joann Stambaugh, Tristan Lim, Rupa Chowdary, Dheeraj Jalluri, Salma K. Jabbour, Sung Kim, Meral Reyhan, Clifford G. Robinson, Wade L. Thorstad, Jehee Isabelle Choi, Robert H. Press, Charles B. Simone, Joe Camaratta, Steve Owens, Drew A. TorigianList of authors in order
- Landing page
-
https://doi.org/10.1002/mp.15854Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.15854Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.15854Direct OA link when available
- Concepts
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Artificial intelligence, Segmentation, Computer science, Contouring, Robustness (evolution), Computer vision, Deep learning, Cognitive neuroscience of visual object recognition, Pattern recognition (psychology), Dice, Medical imaging, Image segmentation, Object (grammar), Biology, Mathematics, Computer graphics (images), Geometry, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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27Total citation count in OpenAlex
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2025: 10, 2024: 8, 2023: 8, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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99Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 24 |
| abstract_inverted_index.measures | 356 |
| abstract_inverted_index.methods, | 14 |
| abstract_inverted_index.modules: | 103 |
| abstract_inverted_index.morphing | 174 |
| abstract_inverted_index.observed | 455 |
| abstract_inverted_index.obtained | 246 |
| abstract_inverted_index.overcome | 44 |
| abstract_inverted_index.patients | 250, 267 |
| abstract_inverted_index.performs | 130 |
| abstract_inverted_index.planning | 93 |
| abstract_inverted_index.practice | 336 |
| abstract_inverted_index.provided | 203 |
| abstract_inverted_index.regions, | 279 |
| abstract_inverted_index.separate | 262 |
| abstract_inverted_index.utilized | 292 |
| abstract_inverted_index.(DL‐D), | 196 |
| abstract_inverted_index.(DL‐R), | 151 |
| abstract_inverted_index.Automatic | 2 |
| abstract_inverted_index.Hausdorff | 365 |
| abstract_inverted_index.automatic | 123 |
| abstract_inverted_index.boundary. | 503 |
| abstract_inverted_index.contours. | 449 |
| abstract_inverted_index.contrast, | 316 |
| abstract_inverted_index.delineate | 207 |
| abstract_inverted_index.employed: | 358 |
| abstract_inverted_index.formulate | 62 |
| abstract_inverted_index.garnering | 30 |
| abstract_inverted_index.implants. | 321 |
| abstract_inverted_index.involving | 254, 265 |
| abstract_inverted_index.localized | 142 |
| abstract_inverted_index.measuring | 380 |
| abstract_inverted_index.multisite | 95 |
| abstract_inverted_index.networks, | 26 |
| abstract_inverted_index.precisely | 116 |
| abstract_inverted_index.radiation | 90 |
| abstract_inverted_index.strengths | 72 |
| abstract_inverted_index.utilizing | 343 |
| abstract_inverted_index.(AAR‐R), | 128 |
| abstract_inverted_index.Background | 1 |
| abstract_inverted_index.DL‐based | 149, 194 |
| abstract_inverted_index.NI‐based | 122 |
| abstract_inverted_index.artifacts, | 314 |
| abstract_inverted_index.artificial | 18 |
| abstract_inverted_index.clinically | 447 |
| abstract_inverted_index.contouring | 385, 420, 432, 466 |
| abstract_inverted_index.efficiency | 38, 378 |
| abstract_inverted_index.facilitate | 224 |
| abstract_inverted_index.illustrate | 84 |
| abstract_inverted_index.integrates | 69 |
| abstract_inverted_index.knowledge. | 59 |
| abstract_inverted_index.pathology, | 319 |
| abstract_inverted_index.reference. | 352 |
| abstract_inverted_index.robustness | 463 |
| abstract_inverted_index.subjective | 369 |
| abstract_inverted_index.tomography | 9 |
| abstract_inverted_index.Conclusions | 450 |
| abstract_inverted_index.application | 88 |
| abstract_inverted_index.coefficient | 362 |
| abstract_inverted_index.combination | 221 |
| abstract_inverted_index.compromised | 37 |
| abstract_inverted_index.containment | 201 |
| abstract_inverted_index.delineation | 195, 500 |
| abstract_inverted_index.distortion, | 318 |
| abstract_inverted_index.established | 346 |
| abstract_inverted_index.evaluation. | 97 |
| abstract_inverted_index.independent | 263 |
| abstract_inverted_index.information | 202, 480 |
| abstract_inverted_index.institution | 264 |
| abstract_inverted_index.limitations | 310 |
| abstract_inverted_index.performance | 85 |
| abstract_inverted_index.recognition | 125, 127, 132, 150, 156 |
| abstract_inverted_index.robustness. | 40 |
| abstract_inverted_index.challenging. | 12 |
| abstract_inverted_index.efficiently. | 471 |
| abstract_inverted_index.high‐level | 31 |
| abstract_inverted_index.information, | 33 |
| abstract_inverted_index.intelligence | 19, 48, 65 |
| abstract_inverted_index.localization | 491 |
| abstract_inverted_index.outperformed | 418 |
| abstract_inverted_index.recognition, | 107 |
| abstract_inverted_index.segmentation | 3, 79 |
| abstract_inverted_index.acceptability | 371, 435 |
| abstract_inverted_index.automatically | 109 |
| abstract_inverted_index.complementary | 71 |
| abstract_inverted_index.computational | 54 |
| abstract_inverted_index.incorporating | 46 |
| abstract_inverted_index.independently | 345 |
| abstract_inverted_index.prospectively | 247 |
| abstract_inverted_index.respectively. | 299, 415 |
| abstract_inverted_index.significantly | 417, 438 |
| abstract_inverted_index.end‐to‐end | 22 |
| abstract_inverted_index.ground‐truth | 347 |
| abstract_inverted_index.training/model | 271 |
| abstract_inverted_index.auto‐contours | 445 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5065713856 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 30 |
| corresponding_institution_ids | https://openalex.org/I79576946 |
| citation_normalized_percentile.value | 0.88091239 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |