Dr. Sushmita Mukherjee Profile

Dr. Sushmita Mukherjee

at Weill Cornell Medicine

SPIE Involvement:

Author

Publications (8)

Proceedings Article | 7 March 2022 Presentation

Kidney cancer diagnosis using deep learning

Andrew Cheng, Michael Icaza, Nicholas Judd, Jason Smith, Sushmita Mukherjee, Manu Jain, Binlin Wu

Proceedings Volume PC11954, PC119540E (2022) https://doi.org/10.1117/12.2610117

KEYWORDS: Kidney, Tumors, Cancer, Image classification, Visual process modeling, Tumor growth modeling, Data modeling, Receivers, Performance modeling, Multiphoton microscopy

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Proceedings Article | 4 March 2019 Paper

Distinguish chromophobe renal cell carcinoma and renal oncocytoma based on analysis of multiphoton microscopic images using convolutional neural network

Michael Icaza, Nicolas Judd, Manu Jain, Sushmita Mukherjee, Binlin Wu

Proceedings Volume 10873, 1087315 (2019) https://doi.org/10.1117/12.2509133

KEYWORDS: Convolutional neural networks, Tumor growth modeling, Image classification, Second-harmonic generation, Tumors, Image fusion, Collagen, Tissues, Performance modeling, 3D image processing

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Proceedings Article | 21 February 2018 Paper

A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

Nicolas Judd, Jason Smith, Manu Jain, Sushmita Mukherjee, Michael Icaza, Ryan Gallagher, Richard Szeligowski, Binlin Wu

Proceedings Volume 10489, 1048919 (2018) https://doi.org/10.1117/12.2288088

KEYWORDS: Tumors, Collagen, Image processing, Neurons, Data modeling, Kidney, Tumor growth modeling, Digital filtering, Image filtering, Second-harmonic generation

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Proceedings Article | 27 April 2016 Paper

A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors

Binlin Wu, Sushmita Mukherjee, Manu Jain

Proceedings Volume 9712, 97121O (2016) https://doi.org/10.1117/12.2213681

KEYWORDS: Kidney, Tissue optics, Tissues, Luminescence, Image resolution, Tumors, Image classification, Second-harmonic generation, Feature selection, Diagnostics

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Proceedings Article | 7 March 2016 Presentation + Paper

Ex vivo applications of multiphoton microscopy in urology

Manu Jain, Sushmita Mukherjee

Proceedings Volume 9703, 97030I (2016) https://doi.org/10.1117/12.2211101

KEYWORDS: Tissues, Multiphoton microscopy, Biopsy, Surgery, Bladder, Kidney, Tumors, Diagnostics, Collagen, Second-harmonic generation

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Background: Routine urological surgery frequently requires rapid on-site histopathological tissue evaluation either during biopsy or intra-operative procedure. However, resected tissue needs to undergo processing, which is not only time consuming but may also create artifacts hindering real-time tissue assessment. Likewise, pathologist often relies on several ancillary methods, in addition to H&E to arrive at a definitive diagnosis. Although, helpful these techniques are tedious and time consuming and often show overlapping results. Therefore, there is a need for an imaging tool that can rapidly assess tissue in real-time at cellular level. Multiphoton microscopy (MPM) is one such technique that can generate histology-quality images from fresh and fixed tissue solely based on their intrinsic autofluorescence emission, without the need for tissue processing or staining.

Design: Fresh tissue sections (neoplastic and non-neoplastic) from biopsy and surgical specimens of bladder and kidney were obtained. Unstained deparaffinized slides from biopsy of medical kidney disease and oncocytic renal neoplasms were also obtained. MPM images were acquired using with an Olympus FluoView FV1000MPE system. After imaging, fresh tissues were submitted for routine histopathology.

Results: Based on the architectural and cellular details of the tissue, MPM could characterize normal components of bladder and kidney. Neoplastic tissue could be differentiated from non-neoplastic tissue and could be further classified as per histopathological convention. Some of the tumors had unique MPM signatures not otherwise seen on H&E sections. Various subtypes of glomerular lesions were identified as well as renal oncocytic neoplasms were differentiated on unstained deparaffinized slides.

Conclusions: We envision MPM to become an integral part of regular diagnostic workflow for rapid assessment of tissue. MPM can be used to evaluate the adequacy of biopsies and triage tissues for ancillary studies. It can also be used as an adjunct to frozen section analysis for intra-operative margin assessment. Further, it can play an important role for pathologist for guiding specimen grossing, selecting tissue for tumor banking and as a rapid ancillary diagnostic tool.

Proceedings Article | 27 April 2015 Paper

Non-invasive discrimination between pancreatic islets and exocrine cells using multiphoton microscopy

Binlin Wu, Ge Li, Mingming Hao, Sushmita Mukherjee

Proceedings Volume 9329, 932935 (2015) https://doi.org/10.1117/12.2083555

KEYWORDS: Luminescence, Multiphoton microscopy, Biopsy, Tissues, Absorption, In vitro testing, Microscopy, Signal detection, Confocal microscopy, Light scattering

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