Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Sirinukunwattana, Korsuk; Pluim, Josien P. W.; Chen, Hao; Qi, Xiaojuan; Heng, Pheng‐Ann; Guo, Yun; Wang, Li Yang; Matuszewski, Bogdan J.; Bruni, Elia; Sanchez, Urko; Böhm, Anton; Ronneberger, Olaf; Cheikh, Bassem Ben; Racoceanu, Daniel; Kainz, Philipp; Pfeiffer, Michael; Urschler, Martin; Snead, David; Rajpoot, Nasir M.

doi:10.48550/arxiv.1603.00275

Cited by 6 publications

(12 citation statements)

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“…Our framework performs well on datasets provided by MICCAI 2015 Gland Segmentation Challenge Contest and achieves the state-of-the-art result (as listed in Table I) among all participants [12]. We rearrange the scores and ranks in this table.…”

Section: Resultsmentioning

confidence: 99%

“…The program for evaluation is given by MICCAI 2015 Gland Segmentation Challenge Contest [12], [13]. The first criterion for evaluation reflets the accuracy of gland detection which is called F1score.…”

Section: Discussionmentioning

confidence: 99%

“…Our method is evaluated on the dataset provided by MIC-CAI 2015 Gland Segmentation Challenge Contest [12], [13]…”

Section: Experiments a Datasetmentioning

confidence: 99%

“…We make use of multichannel learning, region, boundary and location cues using convolutional neural networks with deep supervision, and solve the instance segmentation issue in the gland histology image. Our algorithm is evaluated on the dataset provided by MICCAI 2015 Gland Segmentation Challenge Contest [12], [13] and achieves state-of-the-art performance among all participants and other popular methods of instance segmentation. We conduct a series of experiments in comparison with other algorithms and proves the superiority of the proposed framework.…”

mentioning

confidence: 99%

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Gland Instance Segmentation by Deep Multichannel Neural Networks

Xu¹,

Li²,

Liu³

et al. 2016

Preprint

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In this paper, we propose a new image instance segmentation method that segments individual glands (instances) in colon histology images. This is a task called instance segmentation that has recently become increasingly important. The problem is challenging since not only do the glands need to be segmented from the complex background, they are also required to be individually identified. Here we leverage the idea of image-to-image prediction in recent deep learning by building a framework that automatically exploits and fuses complex multichannel information, regional, location and boundary patterns in gland histology images. Our proposed system, deep multichannel framework, alleviates heavy feature design due to the use of convolutional neural networks and is able to meet multifarious requirement by altering channels. Compared to methods reported in the 2015 MICCAI Gland Segmentation Challenge and other currently prevalent methods of instance segmentation, we observe state-of-the-art results based on a number of evaluation metrics.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Our method is evaluated on the dataset provided by MIC-CAI 2015 Gland Segmentation Challenge Contest [12], [13]…”

Section: Experiments a Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

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Gland Instance Segmentation by Deep Multichannel Neural Networks

Xu¹,

Li²,

Liu³

et al. 2016

Preprint

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“…We use Gland segmentation (Sirinukunwattana et al 2016), MoNuSeg (Kumar et al 2017(Kumar et al , 2020 and Synapse multiorgan segmentation dataset (Bennett et al 2015) to evaluate our method. Gland segmentation dataset (GlaS) has 85 images for training and 80 for testing.…”

Section: Experiments Datasetsmentioning

confidence: 99%

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

Wang¹,

Cao²,

Wang³

et al. 2021

Preprint

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Most recent semantic segmentation methods adopt a U-Net framework with an encoder-decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to model the global multi-scale context: 1) Not each skip connection setting is effective due to the issue of incompatible feature sets of encoder and decoder stage, even some skip connection negatively influence the segmentation performance; 2) The original U-Net is worse than the one without any skip connection on some datasets. Based on our findings, we propose a new segmentation framework, named UC-TransNet (with a proposed CTrans module in U-Net), from the channel perspective with attention mechanism. Specifically, the CTrans (Channel Transformer) module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channelwise Cross-Attention (named CCA) to guide the fused multiscale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity. Hence, the proposed connection consisting of the CCT and CCA is able to replace the original skip connection to solve the semantic gaps for an accurate automatic medical image segmentation. The experimental results suggest that our UCTransNet produces more precise segmentation performance and achieves consistent improvements over the state-of-the-art for semantic segmentation across different datasets and conventional architectures involving transformer or U-shaped framework. Code: https://github.com/McGregorWwww/UCTransNet.

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Classification of colorectal primer carcinoma from normal colon with mid‐infrared spectra

Borkovits,

Kontsek,

Pesti

et al. 2024

Journal of Chemometrics

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In this project, we used formalin‐fixed paraffin‐embedded (FFPE) tissue samples to measure thousands of spectra per tissue core with Fourier transform mid‐infrared spectroscopy using an FT‐IR imaging system. These cores varied between normal colon (NC) and colorectal primer carcinoma (CRC) tissues. We created a database to manage all the multivariate data obtained from the measurements. Then, we applied classifier algorithms to identify the tissue based on its yielded spectra. For classification, we used the random forest, a support vector machine, XGBoost, and linear discriminant analysis methods, as well as three deep neural networks. We compared two data manipulation techniques using these models and then applied filtering. In the end, we compared model performances via the sum of ranking differences (SRD).

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Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest

Cited by 6 publications

References 0 publications

Gland Instance Segmentation by Deep Multichannel Neural Networks

Gland Instance Segmentation by Deep Multichannel Neural Networks

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

Classification of colorectal primer carcinoma from normal colon with mid‐infrared spectra

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