Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences
The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/14/4947 |
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| author | Jang Pyo Bae Malinda Vania Siyeop Yoon Sojeong Cheon Chang Hwan Yoon Deukhee Lee |
| author_facet | Jang Pyo Bae Malinda Vania Siyeop Yoon Sojeong Cheon Chang Hwan Yoon Deukhee Lee |
| author_sort | Jang Pyo Bae |
| collection | DOAJ |
| description | The creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 ± 0.37 mm of LV SSD, 2.25 ± 0.51 mm* of LA SSD, and 2.06 ± 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% ± 4.27%***, where * <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.05, ** <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.01, and *** <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.001. All p values derive from paired <i>t</i>-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation. |
| first_indexed | 2024-03-10T18:24:14Z |
| format | Article |
| id | doaj.art-f445fba88b5946c6b7934b5c33bacf50 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T18:24:14Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f445fba88b5946c6b7934b5c33bacf502023-11-20T07:12:10ZengMDPI AGApplied Sciences2076-34172020-07-011014494710.3390/app10144947Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic CorrespondencesJang Pyo Bae0Malinda Vania1Siyeop Yoon2Sojeong Cheon3Chang Hwan Yoon4Deukhee Lee5Center for Medical Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Medical Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Medical Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCenter for Medical Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaCardiovascular Center, Seoul National University Bundang Hospital, Seoul 13620, KoreaCenter for Medical Robotics, Korea Institute of Science and Technology, 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, KoreaThe creation of 3D models for cardiac mapping systems is time-consuming, and the models suffer from issues with repeatability among operators. The present study aimed to construct a double-shaped model composed of the left ventricle and left atrium. We developed cascaded-regression-based segmentation software with probabilistic point and appearance correspondence. Group-wise registration of point sets constructs the point correspondence from probabilistic matches, and the proposed method also calculates appearance correspondence from these probabilistic matches. Final point correspondence of group-wise registration constructed independently for three surfaces of the double-shaped model. Stochastic appearance selection of cascaded regression enables the effective construction in the aspect of memory usage and computation time. The two correspondence construction methods of active appearance models were compared in terms of the paired segmentation of the left atrium (LA) and left ventricle (LV). The proposed method segmented 35 cardiac CTs in six-fold cross-validation, and the symmetric surface distance (SSD), Hausdorff distance (HD), and Dice coefficient (DC), were used for evaluation. The proposed method produced 1.88 ± 0.37 mm of LV SSD, 2.25 ± 0.51 mm* of LA SSD, and 2.06 ± 0.34 mm* of the left heart (LH) SSD. Additionally, DC was 80.45% ± 4.27%***, where * <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.05, ** <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.01, and *** <inline-formula> <math display="inline"> <semantics> <mrow> <mi>p</mi> <mo><</mo> </mrow> </semantics> </math> </inline-formula>0.001. All p values derive from paired <i>t</i>-tests comparing iterative closest registration with the proposed method. In conclusion, the authors developed a cascaded regression framework for 3D cardiac CT segmentation.https://www.mdpi.com/2076-3417/10/14/4947cascaded regressiongroup-wise correspondence constructioncardiac CTheart segmentation |
| spellingShingle | Jang Pyo Bae Malinda Vania Siyeop Yoon Sojeong Cheon Chang Hwan Yoon Deukhee Lee Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences Applied Sciences cascaded regression group-wise correspondence construction cardiac CT heart segmentation |
| title | Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences |
| title_full | Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences |
| title_fullStr | Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences |
| title_full_unstemmed | Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences |
| title_short | Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences |
| title_sort | cascaded regression based segmentation of cardiac ct under probabilistic correspondences |
| topic | cascaded regression group-wise correspondence construction cardiac CT heart segmentation |
| url | https://www.mdpi.com/2076-3417/10/14/4947 |
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