Multi-scale learning based segmentation of glands in digital colonrectal pathology images.
Digital histopathological images provide detailed spatial information of the tissue at micrometer resolution. Among the available contents in the pathology images, meso-scale information, such as the gland morphology, texture, and distribution, are useful diagnostic features. In this work, focusing on the colon-rectal cancer tissue samples, we propose a multi-scale learning based segmentation scheme for the glands in the colon-rectal digital pathology slides. The algorithm learns the gland and non-gland textures from a set of training images in various scales through a sparse dictionary representation. After the learning step, the dictionaries are used collectively to perform the classification and segmentation for the new image.
Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A.
Yi
Y
Gao
The Upper School of Buckingham Browne & Nichols, MA, U.S.A.
William
W
Liu
Department of Biomedical Engineering, Stony Brook University, NY, U.S.A.
Shipra
S
Arjun
Department of Biomedical Informatics, Stony Brook University, NY, U.S.A.
Liangjia
L
Zhu
Department of Biomedical Informatics, Stony Brook University, NY, U.S.A.
Vadim
V
Ratner
Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A.
Tahsin
T
Kurc
Department of Biomedical Informatics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A.
Joel
J
Saltz
Department of Applied Mathematics & Statistics, Stony Brook University, NY, U.S.A; Department of Computer Science, Stony Brook University, NY, U.S.A; Department of Biomedical Informatics, Stony Brook University, NY, U.S.A.
Allen
A
Tannenbaum
dictionary learning; digital pathology; gland segmentation; texture