TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

Wasserthal, Jakob; Breit, Hanns‐Christian; Meyer, Manfred T.; Pradella, Maurice; Hinck, Daniel; Sauter, Alexander; Heye, Tobias; Boll, Daniel T.; Cyriac, Joshy; Yang, Shan; Bach, Michael; Segeroth, Martin

doi:10.1148/ryai.230024

Cited by 222 publications

(94 citation statements)

References 19 publications

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“…In this study, the LungCTAnalyzer module uses a nnU-Net TotalSegmentator AI tool (11), which allowed for autonomous computer analysis including lung lobes without human intervention. The use of AIpowered tools like the nnU-Net TotalSegmentator can provide several bene ts, including reduced time spent on image analysis, increased consistency in assessments, and potentially improved patient outcomes due to faster and more accurate diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“…Utilizing the TotalSegmentator (nnU-Net) (11) for segmentation, we were able to automatically generate segments corresponding to the trachea and the left erector spinae muscle in the CT datasets. We then evaluated the median Houns eld units (HU) of both segmented structures in both scanners (Toshiba: Trachea: -931.6 Muscle: 22.68 [mean HU], Siemens: Trachea: -958.2 Muscle: 24.25 [mean HU] ) A Python function then standardized CT scans by calibrating Houns eld unit (HU) values for air and muscle.…”

Section: Data Calibrationmentioning

confidence: 99%

See 1 more Smart Citation

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

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Background: The current study aimed to investigate the distribution and extent of lung involvement in patients with COVID-19, assess the relationship between lung involvement and the need for intensive care unit (ICU) admission and compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Lung involvement was assessed using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against the expert rating provided by a radiological expert. Results: The results showed a higher degree of infiltration and collapse in the left lower lobe compared to the upper lobe (p < 0.05) and a similar pattern in the right lower lobes (p < 0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobes demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, no significant difference in COVID-19 involvement was found when comparing the anterior and posterior lungs. The middle third displayed greater COVID-19 involvement than the upper and lower thirds. Patients requiring ICU admission during their inpatient treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by a radiological expert. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes and middle third, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

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Section: Discussionmentioning

confidence: 99%

Section: Data Calibrationmentioning

confidence: 99%

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

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“…Another deep learning model for segmenting CT images based on the nnU-NET algorithm called Total-Segmentator (CT-DL(TS)) which is publicly available and has demonstrated high accuracy as measured by the DICE coefficient [22], was also applied on the dataset.…”

Section: Deep Learning Segmentationmentioning

confidence: 99%

“…These provide information to enable segmentation of the investigated organ and the trained model can be rapidly applied to a new image [14]. Since no registration is required between the training dataset and the new image, this method has been broadly applied in CT segmentation of abdominal organs with studies reporting high similarity with manual segmentations [12,[15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images

Krokos,

Kotwal,

Malaih

et al. 2024

Biomed. Phys. Eng. Express

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Utilisation of whole-organ volumes to extract anatomical and functional information from computed tomography (CT) and positron emission tomography (PET) images may provide key information for the treatment and follow-up of cancer patients. However, manual organ segmentation, is laborious and time-consuming.In this study, a CT-based deep learning method and a multi-atlas method were evaluated for segmenting the liver and spleen on CT images to extract quantitative tracer information from Fluorine-18 fluorodeoxyglucose ([18F]FDG) PET images of 50 patients with advanced Hodgkin lymphoma (HL). Manual segmentation was used as the reference method. The two automatic methods were also compared with a manually defined volume of interest (VOI) within the organ, a technique commonly performed in clinical settings.Both automatic methods provided accurate CT segmentations, with the deep learning method outperforming the multi-atlas with a DICE coefficient of 0.93 ± 0.03 (mean ± standard deviation) in liver and 0.87 ± 0.17 in spleen compared to 0.87 ± 0.05 (liver) and 0.78 ± 0.11 (spleen) for the multi-atlas. Similarly, a mean relative error of -3.2% for the liver and -3.4% for the spleen across patients was found for the mean standardized uptake value (SUVmean) using the deep learning regions while the corresponding errors for the multi-atlas method were -4.7% and -9.2%, respectively. For the maximum SUV (SUVmax), both methods resulted in higher than 20% overestimation due to the extension of organ boundaries to include neighbouring, high-uptake regions. The conservative VOI method which did not extend into neighbouring tissues, provided a more accurate SUVmax estimate.In conclusion, the automatic, and particularly the deep learning method could be used to rapidly extract information of the SUVmean within the liver and spleen. However, activity from neighbouring organs and lesions can lead to high biases in SUVmax and a manually defined volume of interest should be considered instead.

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“…During a preliminary scan of all datasets, it became evident that the CT scans exhibited some heterogeneity in HU range, quality, windowing, and the presence of artifacts. Utilizing the TotalSegmentator (nnU-Net) (11) for segmentation, we were able to automatically generate segments corresponding to the trachea and the left erector spinae muscle in the CT datasets. We then evaluated the median Houns eld units (HU) of both segmented structures in both scanners (Toshiba: Trachea: -931.…”

Section: Data Calibrationmentioning

confidence: 99%

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

Bumm

Zaffino

Lassó

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The current study aimed to investigate the distribution and extent of lung involvement in patients with COVID-19, assess the relationship between lung involvement and the need for intensive care unit (ICU) admission and compare the performance of computer analysis with the judgment of radiological experts. Methods: A total of 81 patients from an open-source COVID database with confirmed COVID-19 infection were included in the study. Three patients were excluded. Lung involvement was assessed in 78 patients using computed tomography (CT) scans, and the extent of infiltration and collapse was quantified across various lung lobes and regions. The associations between lung involvement and ICU admission were analyzed. Additionally, the computer analysis of COVID-19 involvement was compared against the expert rating provided by a radiological expert. Results: The results showed a higher degree of infiltration and collapse in the left lower lobe compared to the upper lobe (p < 0.05) and a similar pattern in the right lower lobes (p < 0.05). No significant difference was detected in the COVID-19-related involvement of the left and right lower lobes. The right middle lobes demonstrated lower involvement compared to the right lower lobes (p < 0.05). When examining the regions, no significant difference in COVID-19 involvement was found when comparing the anterior and posterior lungs. The middle third displayed greater COVID-19 involvement than the upper and lower thirds. Patients requiring ICU admission during their inpatient treatment exhibited significantly higher COVID-19 involvement in their lung parenchyma according to computer analysis, compared to patients who remained in general wards. Patients with more than 40% COVID-19 involvement were almost exclusively treated in intensive care. A high correlation was observed between computer detection of COVID-19 affections and expert rating by radiological experts. Conclusion: The findings suggest that the extent of lung involvement, particularly in the lower lobes and middle third, may be associated with the need for ICU admission in patients with COVID-19. Computer analysis showed a high correlation with expert rating, highlighting its potential utility in clinical settings for assessing lung involvement. This information may help guide clinical decision-making and resource allocation during ongoing or future pandemics. Further studies with larger sample sizes are warranted to validate these findings.

show abstract

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

Cited by 222 publications

References 19 publications

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

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TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images

Cited by 222 publications

References 19 publications

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [18F]FDG PET-CT images

From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases

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Product

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About

Evaluation of manual and automated approaches for segmentation and extraction of quantitative indices from [¹⁸F]FDG PET-CT images