1
|
Jang J, Kim YH, Westgate B, Zong Y, Hallinan C, Akalin A, Lee K. Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier. Sci Rep 2023; 13:13525. [PMID: 37598279 PMCID: PMC10439921 DOI: 10.1038/s41598-023-40652-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 08/16/2023] [Indexed: 08/21/2023] Open
Abstract
Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However, about 10% of initial FNA biopsy samples from patients are non-diagnostic and require repeated FNA, which delays the diagnosis and appropriate care. On-site evaluation of the FNA sample can be performed to filter out non-diagnostic FNA samples. Unfortunately, it involves a time-consuming staining process, and a cytopathologist has to be present at the time of FNA. To bypass the staining process and expert interpretation of FNA specimens at the clinics, we developed a deep learning-based ensemble model termed FNA-Net that allows in situ screening of adequacy of unstained thyroid FNA samples smeared on a glass slide which can decrease the non-diagnostic rate in thyroid FNA. FNA-Net combines two deep learning models, a patch-based whole slide image classifier and Faster R-CNN, to detect follicular clusters with high precision. Then, FNA-Net classifies sample slides to be non-diagnostic if the total number of detected follicular clusters is less than a predetermined threshold. With bootstrapped sampling, FNA-Net achieved a 0.81 F1 score and 0.84 AUC in the precision-recall curve for detecting the non-diagnostic slides whose follicular clusters are less than six. We expect that FNA-Net can dramatically reduce the diagnostic cost associated with FNA biopsy and improve the quality of patient care.
Collapse
Affiliation(s)
- Junbong Jang
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Young H Kim
- Department of Radiology, University of Massachusetts Medical School, Worcester, MA, 01655, USA.
| | - Brian Westgate
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA
| | - Yang Zong
- Department of Pathology, University of Massachusetts Medical School, Worcester, MA, 01655, USA
| | - Caleb Hallinan
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, 02115, USA
| | - Ali Akalin
- Department of Pathology, University of Massachusetts Medical School, Worcester, MA, 01655, USA.
| | - Kwonmoo Lee
- Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
- Vascular Biology Program, Boston Children's Hospital, Boston, MA, 02115, USA.
- Department of Surgery, Harvard Medical School, Boston, MA, 02115, USA.
| |
Collapse
|
2
|
Jang J, Lee K, Kim TK. Unsupervised Contour Tracking of Live Cells by Mechanical and Cycle Consistency Losses. PROCEEDINGS. IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION 2023; 2023:227-236. [PMID: 38250674 PMCID: PMC10798679 DOI: 10.1109/cvpr52729.2023.00030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Analyzing the dynamic changes of cellular morphology is important for understanding the various functions and characteristics of live cells, including stem cells and metastatic cancer cells. To this end, we need to track all points on the highly deformable cellular contour in every frame of live cell video. Local shapes and textures on the contour are not evident, and their motions are complex, often with expansion and contraction of local contour features. The prior arts for optical flow or deep point set tracking are unsuited due to the fluidity of cells, and previous deep contour tracking does not consider point correspondence. We propose the first deep learning-based tracking of cellular (or more generally viscoelastic materials) contours with point correspondence by fusing dense representation between two contours with cross attention. Since it is impractical to manually label dense tracking points on the contour, unsupervised learning comprised of the mechanical and cyclical consistency losses is proposed to train our contour tracker. The mechanical loss forcing the points to move perpendicular to the contour effectively helps out. For quantitative evaluation, we labeled sparse tracking points along the contour of live cells from two live cell datasets taken with phase contrast and confocal fluorescence microscopes. Our contour tracker quantitatively outperforms compared methods and produces qualitatively more favorable results. Our code and data are publicly available at https://github.com/JunbongJang/contour-tracking/.
Collapse
Affiliation(s)
| | - Kwonmoo Lee
- Boston Children’s Hospital, Harvard Medical School
| | | |
Collapse
|