Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features

Rezaei, Safiyeh; Emami, Ali; Zarrabi, Hamidreza; Rafiei, Shima; Najarian, Kayvan; Karimi, Nader; Samavi, Shadrokh; Soroushmehr, S. M. Reza

doi:10.1109/embc.2019.8856776

Cited by 17 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Stain decomposition is achieved based on an orthonormal transformation of RGB channels. It is shown that the boundaries of glands in the hematoxylin component are more visible [9].…”

Section: A Preprocessingmentioning

confidence: 99%

See 1 more Smart Citation

Gland Segmentation in Histopathological Images by Deep Neural Network

Rezaei

Emami

Karimi

et al. 2020

2020 25th International Computer Conference, Computer Society of Iran (CSICC)

Self Cite

View full text Add to dashboard Cite

Histology method is vital in the diagnosis and prognosis of cancers and many other diseases. For the analysis of histopathological images, we need to detect and segment all gland structures. These images are very challenging, and the task of segmentation is even challenging for specialists. Segmentation of glands determines the grade of cancer such as colon, breast, and prostate. Given that deep neural networks have achieved high performance in medical images, we propose a method based on the LinkNet network for gland segmentation. We found the effects of using different loss functions. By using Warwick-Qu dataset, which contains two test sets and one train set, we show that our approach is comparable to state-of-the-art methods. Finally, it is shown that enhancing the gland edges and the use of hematoxylin components can improve the performance of the proposed model.

show abstract

“…Stain decomposition is achieved based on an orthonormal transformation of RGB channels. It is shown that the boundaries of glands in the hematoxylin component are more visible [9].…”

Section: A Preprocessingmentioning

confidence: 99%

“…For a better representation of the color image information, stain decomposition is used. Rezaei et al [9] propose using handcrafted features and Hematoxylin components as network input. They show that the red channel and the Hematoxylin component have more information for segmentation.…”

Section: Introductionmentioning

confidence: 99%

Gland Segmentation in Histopathological Images by Deep Neural Network

Rezaei

Emami

Karimi

et al. 2020

2020 25th International Computer Conference, Computer Society of Iran (CSICC)

Self Cite

View full text Add to dashboard Cite

show abstract

“…ResNet [55], VGGNet + CNN [56], DBN [57] Analysis of interstitial lung diseases CNN [58] Table 1. Cont.…”

Section: Classificationmentioning

confidence: 99%

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

et al. 2021

View full text Add to dashboard Cite

The need for time and attention, given by the doctor to the patient, due to the increased volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes has encouraged the development of the option to support, constructively and effectively, deep learning models. Deep learning (DL) has experienced an exponential development in recent years, with a major impact on interpretations of the medical image. This has influenced the development, diversification and increase of the quality of scientific data, the development of knowledge construction methods and the improvement of DL models used in medical applications. All research papers focus on description, highlighting, classification of one of the constituent elements of deep learning models (DL), used in the interpretation of medical images and do not provide a unified picture of the importance and impact of each constituent in the performance of DL models. The novelty in our paper consists primarily in the unitary approach, of the constituent elements of DL models, namely, data, tools used by DL architectures or specifically constructed DL architecture combinations and highlighting their “key” features, for completion of tasks in current applications in the interpretation of medical images. The use of “key” characteristics specific to each constituent of DL models and the correct determination of their correlations, may be the subject of future research, with the aim of increasing the performance of DL models in the interpretation of medical images.

show abstract

“…Popular segmentation-related research areas include nuclei segmentation, tumor segmentation, gland segmentation, etc. An accurate nuclei segmentation can unveil a lot of information about cancer diagnostics [3,9,18] while an accurate gland segmentation or organ segmentation is important to study and obtain morphological statistics like volume and shape for quantitative diagnosis [2,11,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network

Rastogi

Khanna

Singh

2021

Neural Comput & Applic

View full text Add to dashboard Cite

The accurate gland segmentation from digitized H&E (hematoxylin and eosin) histology images with a wide range of histologic grades of cancer is quite challenging. The methodologies proposed in recent researches have performed well in segmenting glands from benign subjects but have not given satisfactory results when segmenting glands from malignant cases. The methodology proposed in this paper is based on the symmetric encoder-decoder network which works remarkably well in detecting and segmenting glands in the case of malignant subjects and overall. The proposed pipelines harness the power of multilevel CNN architecture to capture contextual information and concatenation of features from skip connections with upsampled feature maps to improve the localization accuracy thereby improving the precise segmentation accuracy. The raw predicted map is further refined using morphological operators as a post-processing tool. The method is trained and evaluated on the Warwick-QU dataset. The final segmentation results have been compared with the performance of top-performing teams of gland challenge (GlaS) MICCI 2015 along with the recent researches. The final predicted segmentation maps have achieved the F1 score of 0.81 for gland detection, object dice score of 0.82 for segmentation, and Hausdorff distance of 84.18 for gland shape similarity which is akin to or higher than the existing models for the malignant subject. The model has done affably well on the overall dataset too.

show abstract

Gland Segmentation in Histopathology Images Using Deep Networks and Handcrafted Features

Cited by 17 publications

References 12 publications

Gland Segmentation in Histopathological Images by Deep Neural Network

Gland Segmentation in Histopathological Images by Deep Neural Network

Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images

Gland segmentation in colorectal cancer histopathological images using U-net inspired convolutional network

Contact Info

Product

Resources

About