Dual-Channel Active Contour Model for Megakaryocytic Cell Segmentation in Bone Marrow Trephine Histology Images

Song, Tzu-Hsi; Sánchez, Vı́ctor; EIDaly, Hesham; Rajpoot, Nasir M.

doi:10.1109/tbme.2017.2690863

Cited by 48 publications

(28 citation statements)

References 46 publications

(65 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Content may change prior to final publication. The Adaptive Moment Estimation refered as Adam optimizer is shown in equation (5). It is by far the most used optimization algorithm after gradient decent.m t andv t are the bias corrected estimates of first and second moment and η is the learning rate respectively, given bym…”

Section: ) Optimizersmentioning

confidence: 99%

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

et al. 2021

View full text Add to dashboard Cite

Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. The underlying article provides a brief overview of deep convolutional neural architecture, the platforms and applications of deep neural networks, metrics used for empirical evaluation, state-of-the-art semantic segmentation architectures based on a foundational convolution concept, and a review of publicly available medical image datasets highlighting four distinct regions of interest. The article also analyzes the existing work and provides open-ended potential research directions in deep medical image semantic segmentation.

show abstract

Section: ) Optimizersmentioning

confidence: 99%

Deep Neural Architectures for Medical Image Semantic Segmentation: Review

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Spe = TN TN + FP (6) In terms of model evaluation, we use the area under the curve (AUC) as a metric. The AUC is widely used as an essential indicator to confirm the effectiveness of machine learning algorithms and is obtained by integrating the area under the receiver operating characteristic (ROC) curve.…”

Section: Evaluation Metricsmentioning

confidence: 99%

HRED-Net: High-Resolution Encoder-Decoder Network for Fine-Grained Image Segmentation

Lyu

Wang

2020

IEEE Access

View full text Add to dashboard Cite

Accurate segmentation of fine-grained information is an important step in medical image analysis applications. With the development of the encoder-decoder-based networks, various network structures and algorithms have made significant progress in semantic segmentation tasks. This work aims to present a novel high-resolution encoder-decoder network (HRED-Net) for fine-grained image segmentation that is highly accurate for small-scale targets. We design a multiscale context connection module to extract feature information without reducing the resolution, and propose a multiresolution fusion model to fine-tune the final results. In addition, these modules are trained together with a detail-oriented loss function to enhance the model's perception of fine-grained parts. Through experiments on the DRIVE dataset, we found a balance between these modules, and our comparison results show that in addition to the extraction multiscale features, the fusion of multiresolution prediction information is also beneficial for fine-grained segmentation. Our method yielded significant improvements in the accuracy and sensitivity in retinal vessel and lung segmentation tasks.

show abstract

“…To this end, such systems initially transmit the pathology images at a low resolution to account for low bandwidth connections. The users can then select the regions of interest (RoIs), e.g., a cell, groups of cells or multiple tissue subregions, on which they would like to concentrate [6]. The system then transmits the RoIs at full resolution, i.e., at a higher bit rate, so the users can 1) add annotations to them, or 2) access any existing annotations.…”

Section: Introductionmentioning

confidence: 99%