Multimodal Image Analysis of Unstained Histological Sections

Ushikawa, Takuya; Takahashi, Masanobu; Nakano, Masayuki

doi:10.1007/978-3-319-11128-5_54

Cited by 1 publication

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“…We previously reported cell membrane segmentation in histological images for H&E stained sections [8], [9] and unstained sections [10], [11], where segmentation of the cell membrane is difficult due to small differences in color between cell membrane and cytoplasm. Although more than 85% of cell membranes were segmented correctly using deep learning, some parts were not segmented.…”

Section: Introductionmentioning

confidence: 99%

Recursive Additive Complement Networks for Cell Membrane Segmentation in Histological Images

Yamami

Sugimoto

Takahashi

et al. 2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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A recursive additive complement network (RacNet) is introduced to segment cell membranes in histological images as closed lines. Segmenting cell membranes as closed lines is necessary to calculate cell areas and to estimate N/C ratio, which is useful to diagnose early hepatocellular carcinoma. The RacNet is composed of a complement network and an element-wise maximization (EWM) process and is recursively applied to the network output. The complement network complements the lacking parts of cell membranes. The network, however, has a tendency to mistakenly delete some parts of the segmented cell membranes. The EWM process eliminates this unwanted effect. Experiments carried out using unstained hepatic sections showed that the accuracy for segmenting cell membranes as closed lines was significantly improved by using the RacNet. Three imaging methods, bright-field, dark-field, and phase-contrast, were used, as unstained sections show very low contrast in the bright-field imaging commonly used in pathological diagnosis. These imaging methods are available in optical microscopes used by pathologists. Among the three methods, phase-contrast imaging showed the highest accuracy.

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Section: Introductionmentioning

confidence: 99%