Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development

Kauanova, Sholpan; Vorobjev, Ivan A.; James, Alex Pappachen

doi:10.1109/icacci.2017.8126203

Cited by 3 publications

(2 citation statements)

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“…Agricultural farms are unconstrained natural environments or semi-constrained at very best. Machine learning has found intuitive applications in many fields, because of its adaptive learning ability, like in healthcare ( Ronneberger et al, 2015 ; Işın et al, 2016 ; Kauanova et al, 2017 ), autonomous driving ( Fujiyoshi et al, 2019 ; Hofmarcher et al, 2019 ; Imai, 2019 ), and weed and crop detection ( Grinblat et al, 2016 ; Mohanty et al, 2016 ; Dyrmann et al, 2017 ; Kussul et al, 2017 ; Fuentes et al, 2018 ). But very little work has been done in detecting fruits and classifying them according to their ripeness level.…”

Section: Related Workmentioning

confidence: 99%

DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

et al. 2021

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Autonomous harvesters can be used for the timely cultivation of high-value crops such as strawberries, where the robots have the capability to identify ripe and unripe crops. However, the real-time segmentation of strawberries in an unbridled farming environment is a challenging task due to fruit occlusion by multiple trusses, stems, and leaves. In this work, we propose a possible solution by constructing a dynamic feature selection mechanism for convolutional neural networks (CNN). The proposed building block namely a dense attention module (DAM) controls the flow of information between the convolutional encoder and decoder. DAM enables hierarchical adaptive feature fusion by exploiting both inter-channel and intra-channel relationships and can be easily integrated into any existing CNN to obtain category-specific feature maps. We validate our attention module through extensive ablation experiments. In addition, a dataset is collected from different strawberry farms and divided into four classes corresponding to different maturity levels of fruits and one is devoted to background. Quantitative analysis of the proposed method showed a 4.1% and 2.32% increase in mean intersection over union, over existing state-of-the-art semantic segmentation models and other attention modules respectively, while simultaneously retaining a processing speed of 53 frames per second.

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Section: Related Workmentioning

confidence: 99%

DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

et al. 2021

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“…In this paper we focus on semantic segmentation. Image segmentation has applications in health care for detecting diseases or cancer cells [1][2][3][4], in agriculture for weed and crop detection or detecting plant diseases [5][6][7][8][9], in autonomous driving for detecting traffic signals, cars, pedestrians [10][11][12], and in other numerous fields of artificial intelligence (AI) [13]. It also poses a main obstacle in the further advancements of computer vision, that we need to overcome.…”

Section: Introductionmentioning

confidence: 99%

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

et al. 2020

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Supervised semantic segmentation algorithms have been a hot area of exploration recently, but now the attention is being drawn towards completely unsupervised semantic segmentation. In an unsupervised framework, neither the targets nor the ground truth labels are provided to the network. That being said, the network is unaware about any class instance or object present in the given data sample. So, we propose a convolutional neural network (CNN) based architecture for unsupervised segmentation. We used the squeeze and excitation network, due to its peculiar ability to capture the features’ interdependencies, which increases the network’s sensitivity to more salient features. We iteratively enable our CNN architecture to learn the target generated by a graph-based segmentation method, while simultaneously preventing our network from falling into the pit of over-segmentation. Along with this CNN architecture, image enhancement and refinement techniques are exploited to improve the segmentation results. Our proposed algorithm produces improved segmented regions that meet the human level segmentation results. In addition, we evaluate our approach using different metrics to show the quantitative outperformance.

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A Proposed Method for Background Subtraction and Adding Scribbles in Video Matting

El-Seddek

2021

2021 International Telecommunications Conference (ITC-Egypt)

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Automated image segmentation for detecting cell spreading for metastasizing assessments of cancer development

Cited by 3 publications

References 12 publications

DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

DAM: Hierarchical Adaptive Feature Selection Using Convolution Encoder Decoder Network for Strawberry Segmentation

SEEK: A Framework of Superpixel Learning with CNN Features for Unsupervised Segmentation

A Proposed Method for Background Subtraction and Adding Scribbles in Video Matting

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