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Villanueva-Miranda I, Rong R, Quan P, Wen Z, Zhan X, Yang DM, Chi Z, Xie Y, Xiao G. Enhancing Medical Imaging Segmentation with GB-SAM: A Novel Approach to Tissue Segmentation Using Granular Box Prompts. Cancers (Basel) 2024; 16:2391. [PMID: 39001452 PMCID: PMC11240495 DOI: 10.3390/cancers16132391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 06/23/2024] [Accepted: 06/25/2024] [Indexed: 07/16/2024] Open
Abstract
Recent advances in foundation models have revolutionized model development in digital pathology, reducing dependence on extensive manual annotations required by traditional methods. The ability of foundation models to generalize well with few-shot learning addresses critical barriers in adapting models to diverse medical imaging tasks. This work presents the Granular Box Prompt Segment Anything Model (GB-SAM), an improved version of the Segment Anything Model (SAM) fine-tuned using granular box prompts with limited training data. The GB-SAM aims to reduce the dependency on expert pathologist annotators by enhancing the efficiency of the automated annotation process. Granular box prompts are small box regions derived from ground truth masks, conceived to replace the conventional approach of using a single large box covering the entire H&E-stained image patch. This method allows a localized and detailed analysis of gland morphology, enhancing the segmentation accuracy of individual glands and reducing the ambiguity that larger boxes might introduce in morphologically complex regions. We compared the performance of our GB-SAM model against U-Net trained on different sizes of the CRAG dataset. We evaluated the models across histopathological datasets, including CRAG, GlaS, and Camelyon16. GB-SAM consistently outperformed U-Net, with reduced training data, showing less segmentation performance degradation. Specifically, on the CRAG dataset, GB-SAM achieved a Dice coefficient of 0.885 compared to U-Net's 0.857 when trained on 25% of the data. Additionally, GB-SAM demonstrated segmentation stability on the CRAG testing dataset and superior generalization across unseen datasets, including challenging lymph node segmentation in Camelyon16, which achieved a Dice coefficient of 0.740 versus U-Net's 0.491. Furthermore, compared to SAM-Path and Med-SAM, GB-SAM showed competitive performance. GB-SAM achieved a Dice score of 0.900 on the CRAG dataset, while SAM-Path achieved 0.884. On the GlaS dataset, Med-SAM reported a Dice score of 0.956, whereas GB-SAM achieved 0.885 with significantly less training data. These results highlight GB-SAM's advanced segmentation capabilities and reduced dependency on large datasets, indicating its potential for practical deployment in digital pathology, particularly in settings with limited annotated datasets.
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Affiliation(s)
- Ismael Villanueva-Miranda
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Peiran Quan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Zhuoyu Wen
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Zhikai Chi
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Wu DY, Fang YV, Vo DT, Spangler A, Seiler SJ. Detailed Image Data Quality and Cleaning Practices for Artificial Intelligence Tools for Breast Cancer. JCO Clin Cancer Inform 2024; 8:e2300074. [PMID: 38552191 PMCID: PMC10994436 DOI: 10.1200/cci.23.00074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 11/30/2023] [Accepted: 02/13/2024] [Indexed: 04/02/2024] Open
Abstract
Standardizing image-data preparation practices to improve accuracy/consistency of AI diagnostic tools.
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Affiliation(s)
- Dolly Y. Wu
- Volunteer Services, UT Southwestern Medical Center, Dallas, TX
| | - Yisheng V. Fang
- Department of Pathology, UT Southwestern Medical Center, Dallas, TX
| | - Dat T. Vo
- Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
| | - Ann Spangler
- Retired, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX
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Greenberg A, Samueli B, Farkash S, Zohar Y, Ish-Shalom S, Hagege RR, Hershkovitz D. Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners. Diagn Pathol 2024; 19:26. [PMID: 38321431 PMCID: PMC10845737 DOI: 10.1186/s13000-024-01452-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
BACKGROUND Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance. METHODS The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast. RESULTS The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners. CONCLUSIONS The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.
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Affiliation(s)
- Ariel Greenberg
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel.
| | - Benzion Samueli
- Department of Pathology, Soroka University Medical Center, 76 Wingate Street, 8486614, Be'er Sheva, Israel
| | - Shai Farkash
- Department of Pathology, Emek Medical Center, Yitshak Rabin Boulevard 21, 1834111, Afula, Israel
| | - Yaniv Zohar
- Department of Pathology, Rambam Medical Center, 8 Haalia Hashnia, 3525408, Haifa, Israel
| | - Shahar Ish-Shalom
- Department of Pathology, Kaplan Medical Center, Pasternak St. P.O.B. 1, 76100, Rehovot, Israel
| | - Rami R Hagege
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
| | - Dov Hershkovitz
- Institute of Pathology, Tel-Aviv Sourasky Medical Center, 6 Weizmann Street, 6423906, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel-Aviv University, Ramat Aviv 69978, Tel-Aviv, Israel
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