Segmentation of Bacterial Cells in Biofilms Using an Overlapped Ellipse Fitting Technique

Abeyrathna, Dilanga; Life, Terrance; Rauniyar, Shailabh; Ragi, Shankarachary; Sani, Rajesh K.; Chundi, Parvathi

doi:10.1109/bibm52615.2021.9669774

Cited by 7 publications

(6 citation statements)

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“…In [42], the authors aim to measure the number and length of bacterial cells and the overall area covered by biofilms. They claim to have solved the object segmentation problem that occurs in closely located or overlapping cells.…”

Section: ) Cnn-based Architecturesmentioning

confidence: 99%

“…In [36], the authors propose a finetuning-based supervised learning approach for pathogenic bacteria identification. Similarly, the authors in [30], [42], and [32] also made use of supervised learning techniques.…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

“…An academic dataset with 66 images and two classes was divided into validation, testing, and testing data. The authors in [42] used a dataset of 290 SEM images. The data were pre-processed to remove any noise and resized to 512x512.…”

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

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A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro¹

2022

IJACSA

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Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. A large number of studies are published annually on deep learning techniques. The focus of this paper is on bacteria detection, identification, and classification. This paper presents a systematic literature review that synthesizes the evidence related to bacteria colony identification and detection published in the year 2021. The aim is to aggregate, analyse, and summarize the evidence related to deep learning detection, identification, and classification of bacteria and bacteria colonies. The significance is that the review will help experts and technicians to understand how deep learning techniques can apply in this regard and potentially further support more accurate detection of bacteria types. A total of 38 studies are analysed. The majority of the published studies focus on supervised-learningbased convolutional neural networks. Furthermore, a large number of studies make use of laboratory-prepared datasets as compared to open-source and industrial datasets. The results also indicate a lack of tools, which is a barrier in adapting academic research in industrial settings.

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Section: ) Cnn-based Architecturesmentioning

confidence: 99%

Section: Rq 12 Which Types Of Learning Have Been Applied?mentioning

confidence: 99%

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A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Nagro¹

2022

IJACSA

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“…For instance, while reading retinal images to identify unhealthy areas, it is common for graders (with ophthalmology training) to discuss each image at length to carefully resolve several confounding and subtle image attributes [12][13][14]. Labeling cells, cell clusters, and microbial byproducts in biofilms take up to two days per image on average [15][16][17]. Therefore, it is highly beneficial to develop high-performance deep segmentation networks that can train with scantly annotated training data.…”

Section: Introductionmentioning

confidence: 99%

“…Now consider the pixels that are not in Target. The pixel-level labels for these pixels in t p can be decided using either the previous model M i−1 or the current model M i (lines [11][12][13][14][15][16][17]. Let p xy be a pixel in x th row and y th column of t p .…”

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confidence: 99%

Semantic Image Segmentation Using Scant Pixel Annotations

Chakravarthy

Abeyrathna

Subramaniam

et al. 2022

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The success of deep networks for the semantic segmentation of images is limited by the availability of annotated training data. The manual annotation of images for segmentation is a tedious and time-consuming task that often requires sophisticated users with significant domain expertise to create high-quality annotations over hundreds of images. In this paper, we propose the segmentation with scant pixel annotations (SSPA) approach to generate high-performing segmentation models using a scant set of expert annotated images. The models are generated by training them on images with automatically generated pseudo-labels along with a scant set of expert annotated images selected using an entropy-based algorithm. For each chosen image, experts are directed to assign labels to a particular group of pixels, while a set of replacement rules that leverage the patterns learned by the model is used to automatically assign labels to the remaining pixels. The SSPA approach integrates active learning and semi-supervised learning with pseudo-labels, where expert annotations are not essential but generated on demand. Extensive experiments on bio-medical and biofilm datasets show that the SSPA approach achieves state-of-the-art performance with less than 5% cumulative annotation of the pixels of the training data by the experts.

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