PMID- 31435950
OWN - NLM
STAT- MEDLINE
DCOM- 20200320
LR  - 20200320
IS  - 2473-4209 (Electronic)
IS  - 0094-2405 (Linking)
VI  - 46
IP  - 11
DP  - 2019 Nov
TI  - ALTIS: A fast and automatic lung and trachea CT-image segmentation method.
PG  - 4970-4982
LID - 10.1002/mp.13773 [doi]
AB  - PURPOSE: The automated segmentation of each lung and trachea in CT scans is 
      commonly taken as a solved problem. Indeed, existing approaches may easily fail 
      in the presence of some abnormalities caused by a disease, trauma, or previous 
      surgery. For robustness, we present ALTIS (implementation is available at 
      http://lids.ic.unicamp.br/downloads) - a fast automatic lung and trachea CT-image 
      segmentation method that relies on image features and relative shape- and 
      intensity-based characteristics less affected by most appearance variations of 
      abnormal lungs and trachea. METHODS: ALTIS consists of a sequence of image 
      foresting transforms (IFTs) organized in three main steps: (a) lung-and-trachea 
      extraction, (b) seed estimation inside background, trachea, left lung, and right 
      lung, and (c) their delineation such that each object is defined by an 
      optimum-path forest rooted at its internal seeds. We compare ALTIS with two 
      methods based on shape models (SOSM-S and MALF), and one algorithm based on 
      seeded region growing (PTK). RESULTS: The experiments involve the highest number 
      of scans found in literature - 1255 scans, from multiple public data sets 
      containing many anomalous cases, being only 50 normal scans used for training and 
      1205 scans used for testing the methods. Quantitative experiments are based on 
      two metrics, DICE and ASSD. Furthermore, we also demonstrate the robustness of 
      ALTIS in seed estimation. Considering the test set, the proposed method achieves 
      an average DICE of 0.987 for both lungs and 0.898 for the trachea, whereas an 
      average ASSD of 0.938 for the right lung, 0.856 for the left lung, and 1.316 for 
      the trachea. These results indicate that ALTIS is statistically more accurate and 
      considerably faster than the compared methods, being able to complete 
      segmentation in a few seconds on modern PCs. CONCLUSION: ALTIS is the most 
      effective and efficient choice among the compared methods to segment left lung, 
      right lung, and trachea in anomalous CT scans for subsequent detection, 
      segmentation, and quantitative analysis of abnormal structures in the lung 
      parenchyma and pleural space.
CI  - (c) 2019 American Association of Physicists in Medicine.
FAU - Sousa, Azael M
AU  - Sousa AM
AD  - Laboratory of Image Data Science, Institute of Computing, University of Campinas, 
      Campinas, Brazil.
FAU - Martins, Samuel B
AU  - Martins SB
AD  - Laboratory of Image Data Science, Institute of Computing, University of Campinas, 
      Campinas, Brazil.
FAU - Falcao, Alexandre X
AU  - Falcao AX
AD  - Laboratory of Image Data Science, Institute of Computing, University of Campinas, 
      Campinas, Brazil.
FAU - Reis, Fabiano
AU  - Reis F
AD  - School of Medical Sciences, University of Campinas, Campinas, Brazil.
FAU - Bagatin, Ericson
AU  - Bagatin E
AD  - School of Medical Sciences, University of Campinas, Campinas, Brazil.
FAU - Irion, Klaus
AU  - Irion K
AD  - Department of Radiology, Manchester University NHS, Campinas, Brazil.
LA  - eng
GR  - CAPES/
GR  - 303808/2018-7/CNPq/
GR  - NVIDIA/
GR  - 2014/12236-1/FAPESP/
PT  - Journal Article
DEP - 20190911
PL  - United States
TA  - Med Phys
JT  - Medical physics
JID - 0425746
SB  - IM
MH  - Algorithms
MH  - Automation
MH  - Humans
MH  - Image Processing, Computer-Assisted/*methods
MH  - Lung/*diagnostic imaging
MH  - Time Factors
MH  - *Tomography, X-Ray Computed
MH  - Trachea/*diagnostic imaging
OTO - NOTNLM
OT  - CT images of the thorax
OT  - image foresting transform
OT  - mathematical morphology
OT  - medical image segmentation
EDAT- 2019/08/23 06:00
MHDA- 2020/03/21 06:00
CRDT- 2019/08/23 06:00
PHST- 2018/12/21 00:00 [received]
PHST- 2019/07/22 00:00 [revised]
PHST- 2019/07/30 00:00 [accepted]
PHST- 2019/08/23 06:00 [pubmed]
PHST- 2020/03/21 06:00 [medline]
PHST- 2019/08/23 06:00 [entrez]
AID - 10.1002/mp.13773 [doi]
PST - ppublish
SO  - Med Phys. 2019 Nov;46(11):4970-4982. doi: 10.1002/mp.13773. Epub 2019 Sep 11.