Deep transfer learning methods for colon cancer classification in confocal laser microscopy images

TY - JOUR

T1 - Deep transfer learning methods for colon cancer classification in confocal laser microscopy images

AU - Gessert, Nils

AU - Bengs, Marcel

AU - Wittig, Lukas

AU - Drömann, Daniel

AU - Keck, Tobias

AU - Schlaefer, Alexander

AU - Ellebrecht, David B

PY - 2019/11

Y1 - 2019/11

N2 - PURPOSE: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback.METHODS: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue.RESULTS: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1, and in the colon the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks.CONCLUSIONS: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.

AB - PURPOSE: The gold standard for colorectal cancer metastases detection in the peritoneum is histological evaluation of a removed tissue sample. For feedback during interventions, real-time in vivo imaging with confocal laser microscopy has been proposed for differentiation of benign and malignant tissue by manual expert evaluation. Automatic image classification could improve the surgical workflow further by providing immediate feedback.METHODS: We analyze the feasibility of classifying tissue from confocal laser microscopy in the colon and peritoneum. For this purpose, we adopt both classical and state-of-the-art convolutional neural networks to directly learn from the images. As the available dataset is small, we investigate several transfer learning strategies including partial freezing variants and full fine-tuning. We address the distinction of different tissue types, as well as benign and malignant tissue.RESULTS: We present a thorough analysis of transfer learning strategies for colorectal cancer with confocal laser microscopy. In the peritoneum, metastases are classified with an AUC of 97.1, and in the colon the primarius is classified with an AUC of 73.1. In general, transfer learning substantially improves performance over training from scratch. We find that the optimal transfer learning strategy differs for models and classification tasks.CONCLUSIONS: We demonstrate that convolutional neural networks and transfer learning can be used to identify cancer tissue with confocal laser microscopy. We show that there is no generally optimal transfer learning strategy and model as well as task-specific engineering is required. Given the high performance for the peritoneum, even with a small dataset, application for intraoperative decision support could be feasible.

KW - Colonic Neoplasms/classification

KW - Deep Learning

KW - Feasibility Studies

KW - Humans

KW - Microscopy, Confocal/methods

KW - Neoplasm Metastasis

KW - Neural Networks, Computer

KW - Peritoneal Neoplasms/diagnosis

U2 - 10.1007/s11548-019-02004-1

DO - 10.1007/s11548-019-02004-1

M3 - SCORING: Journal article

C2 - 31129859

VL - 14

SP - 1837

EP - 1845

JO - INT J COMPUT ASS RAD

JF - INT J COMPUT ASS RAD

SN - 1861-6410

IS - 11

ER -