A Semiautomated Deep Learning Approach for Pancreas Segmentation

Huang, Meixiang; Huang, Chongfei; Yuan, Jing; Kong, Dexing

doi:10.1155/2021/3284493

Cited by 13 publications

(3 citation statements)

References 39 publications

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“…Current automated pancreas tool are largely restricted to segmentation of the endocrine islet fractions of pancreas and are based on cell nulcei identification, extracting colour features mostly for pancreatic cancer detection (Huang et al 2016, Vu et al 2019, Yang et al 2021). There are other deep learning based methods for pancreas segmentation but again this are largely restricted to cancer diagnosis (Huang et al 2021) but cannot analyze fatty cell infiltration.…”

Section: Discussionmentioning

confidence: 99%

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Naik¹,

Rajan²,

Kalita

2021

Preprint

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Hematoxylin and eosin (H and E) is one of the common histological staining techniques that provides information on the tissue cytoarchitecture. Adipose (fat) cells accumulation in pancreas has been shown to impact beta cell survival and its endocrine function. The current automated tools available for fat analysis are suited for white adipose tissue which is homogeneous and easier to segment unlike heterogeneous tissues such as pancreas where fat cells continue to play critical physiopathological functions. In the current study, we present an automated fat analysis tool, Fatquant, where mathematical formula to calculate diagonal of a square drawn inside circle is utilized for identification and analysis of fat cells in heterogeneous H and E tissue sections. Using histological images of pancreas from a publicly available database, we show an area accuracy overlap of 89-93% between manual versus automated algorithm based fat cell detection.

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Section: Discussionmentioning

confidence: 99%

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Naik¹,

Rajan²,

Kalita

2021

Preprint

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“…Deformable convolution dramatically improves the model's adaptability to irregular and non-rigid structures by adding adaptive offsets to each sampling position of the 2D convolutional kernel [40], [41]. In complex and highly irregular applications like pancreatic segmentation, this method elevated the Dice coefficient to 0.8725 [40], although it requires additional training time.…”

Section: Convolution Block Modificationsmentioning

confidence: 99%

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

Zhang,

Achuthan,

Himel

2024

IEEE Access

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In medical image analysis, segmenting pancreatic CT images presents a significant challenge due to the complex anatomy of the pancreas and the generally low contrast of these images. Accurate pancreas segmentation is crucial in clinical scenarios, particularly for the diagnosis and treatment of pancreatic cancer. The U-Net architecture and its variations have achieved significant progress in deep learningbased image segmentation, especially in the context of pancreatic CT image segmentation. However, there is a noticeable gap in the comprehensive evaluation of their performance, limitations, and potential improvements specifically in this area. This systematic review aims to address this gap in the literature, focusing particularly on U-Net and its variants in pancreatic CT image segmentation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review includes relevant studies published since 2019 in the field of pancreatic segmentation. The findings illuminate the current limitations of these methods and establish a theoretical foundation for future research directions.

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“…DL methods showed more accuracy in results than traditional rule-based methods in various domains, including eHealth systems ( 3 – 5 ). A study proposed a hypothesis that further improving the accuracy of DNN feature selection techniques can be used ( 6 , 7 ). Feature selection is the process of acquiring relevant information and discarding irrelevant ones ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

Arya

Sastry

Motwani

et al. 2022

Front. Public Health

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Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.

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A Semiautomated Deep Learning Approach for Pancreas Segmentation

Cited by 13 publications

References 39 publications

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images

State-of-the-Art and Challenges in Pancreatic CT Segmentation: A Systematic Review of U-Net and Its Variants

A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes

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