Rapid 3D imaging at cellular resolution for digital cytopathology with a multi-camera array scanner (MCAS)
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2024
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| author | Kim, Kanghyun Chaware, Amey Cook, Clare B. Xu, Shiqi Abdelmalak, Monica Cooke, Colin Zhou, Kevin C. Harfouche, Mark Reamey, Paul Saliu, Veton Doman, Jed Dugo, Clay Horstmeyer, Gregor Davis, Richard Taylor-Cho, Ian Foo, Wen-Chi Kreiss, Lucas Jiang, Xiaoyin Sara Horstmeyer, Roarke |
| author_facet | Kim, Kanghyun Chaware, Amey Cook, Clare B. Xu, Shiqi Abdelmalak, Monica Cooke, Colin Zhou, Kevin C. Harfouche, Mark Reamey, Paul Saliu, Veton Doman, Jed Dugo, Clay Horstmeyer, Gregor Davis, Richard Taylor-Cho, Ian Foo, Wen-Chi Kreiss, Lucas Jiang, Xiaoyin Sara Horstmeyer, Roarke |
| contents | Optical microscopy has long been the standard method for diagnosis in cytopathology. Whole slide scanners can image and digitize large sample areas automatically, but are slow, expensive and therefore not widely available. Clinical diagnosis of cytology specimens is especially challenging since these samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54x72 mm^2) at 1.2 and 0.6 μm resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole slide scanners. We used this system to digitize entire cytology samples (scanning three entire slides in 3D in just several minutes) and demonstrate two machine learning techniques to assist pathologists: first, an adenocarcinoma detection model in lung specimens (0.73 recall); second, a slide-level classification model of lung smears (0.969 AUC). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_15722 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rapid 3D imaging at cellular resolution for digital cytopathology with a multi-camera array scanner (MCAS) Kim, Kanghyun Chaware, Amey Cook, Clare B. Xu, Shiqi Abdelmalak, Monica Cooke, Colin Zhou, Kevin C. Harfouche, Mark Reamey, Paul Saliu, Veton Doman, Jed Dugo, Clay Horstmeyer, Gregor Davis, Richard Taylor-Cho, Ian Foo, Wen-Chi Kreiss, Lucas Jiang, Xiaoyin Sara Horstmeyer, Roarke Optics Optical microscopy has long been the standard method for diagnosis in cytopathology. Whole slide scanners can image and digitize large sample areas automatically, but are slow, expensive and therefore not widely available. Clinical diagnosis of cytology specimens is especially challenging since these samples are both spread over large areas and thick, which requires 3D capture. Here, we introduce a new parallelized microscope for scanning thick specimens across extremely wide fields-of-view (54x72 mm^2) at 1.2 and 0.6 μm resolutions, accompanied by machine learning software to rapidly assess these 16 gigapixel scans. This Multi-Camera Array Scanner (MCAS) comprises 48 micro-cameras closely arranged to simultaneously image different areas. By capturing 624 megapixels per snapshot, the MCAS is significantly faster than most conventional whole slide scanners. We used this system to digitize entire cytology samples (scanning three entire slides in 3D in just several minutes) and demonstrate two machine learning techniques to assist pathologists: first, an adenocarcinoma detection model in lung specimens (0.73 recall); second, a slide-level classification model of lung smears (0.969 AUC). |
| title | Rapid 3D imaging at cellular resolution for digital cytopathology with a multi-camera array scanner (MCAS) |
| topic | Optics |
| url | https://arxiv.org/abs/2409.15722 |