Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

Pfaehler, Elisabeth; Mesotten, Liesbet; Kramer, Gem; Thomeer, Michiel; Vanhove, Karolien; Jong, Johan de; Adriaensens, Peter; Hoekstra, Otto S.; Boellaard, Ronald

doi:10.1007/978-3-030-52791-4_1

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…All voxels with a summed probability of more than 1.8 were included in the final tumor segmentation. A more detailed description of the algorithm can be found in Additional file 1 and in Pfaehler et al [25].…”

Section: Textural Feature Segmentation (Tf)mentioning

confidence: 99%

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

et al. 2021

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Background Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability. Results The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68). Conclusion The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

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Section: Textural Feature Segmentation (Tf)mentioning

confidence: 99%

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

et al. 2021

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“…Hereby, all voxels with a summed probability of more than 1.8 were included in the tumor mask. A more detailed description of the algorithm can be found in the supplemental and in Pfaehler et al [25].…”

Section: Textural Feature Segmentation (Tf)mentioning

confidence: 99%

Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Pfaehler

Mesotten

Kramer

et al. 2020

Preprint

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Background: Positron Emission Tomography (PET) is routinely used for cancer staging and treatment follow up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis (TLG) derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two artificial intelligence (AI) based segmentation methods with conventional segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a Convolutional Neural Network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the jaccard coefficient (JC). Additionally, the approaches are applied on a fully independent test-retest dataset. The repeatability of the methods is compared with the repeatability of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV>4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). A TRT% of 0 indicates perfect repeatability and an ICC>0.9 was regarded as representing excellent repeatability.Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73) Both segmentation approaches outperformed together with the MV2 approach the other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1% ) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68).Conclusion: The AI based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

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“…All voxels with a summed probability of more than 1.8 were included in the nal tumor segmentation. A more detailed description of the algorithm can be found in the supplemental and in Pfaehler et al [25].…”

Section: Textural Feature Segmentation (Tf)mentioning

confidence: 99%

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Pfaehler

Mesotten

Kramer

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: Positron Emission Tomography (PET) is routinely used for cancer staging and treatment follow up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV - including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis (TLG) derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge. Methods: In this study, we compare two semi-automatic artificial intelligence (AI) based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a Convolutional Neural Network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard Coefficient (JC). Additionally, the approaches are externally tested on a fully independent test-retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUVMAX, and a SUV>4 segmentation (SUV4). Repeatability is assessed with test-retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC>0.9 was regarded as representing excellent repeatability.Results: The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73) Both segmentation approaches outperformed most other conventional segmentation methods in terms of test-retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUVMAX: 28.1%, SUV4: 18.1% ) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUVMAX: 0.68).Conclusion: The semi-automatic AI based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

show abstract

Textural Feature Based Segmentation: A Repeatable and Accurate Segmentation Approach for Tumors in PET Images

Cited by 5 publications

References 12 publications

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Repeatability of Two Artificial Intelligence Approaches for Tumor Segmentation in PET

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

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