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Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning
BackgroundThe deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei...
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Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)
BackgroundNuclei classification, segmentation, and detection from pathological images are challenging tasks due to cellular heterogeneity in the...
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MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network
BackgroundThe digital pathology images obtain the essential information about the patient’s disease, and the automated nuclei segmentation results...
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Segmentation of Multiple Nuclei from Non-overlap** Immuno-histochemically Stained Histological Hepatic Images
In this paper, we describe an algorithm for accurately segmenting multiple nudfclei from clumps of non-overlap** immuno-histochemically stained...
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Comparison of two segmentation software tools for deep brain stimulation of the subthalamic and ventro-intermedius nuclei
PurposeDeep brain stimulation (DBS) relies on precise targeting of key structures such as the subthalamic nucleus (STN) for Parkinson’s disease (PD)...
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Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling
Thalamic abnormalities have been repeatedly implicated in the pathophysiology of schizophrenia and other neurodevelopmental disorders. Uncovering the...
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High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images
PurposeIncreasing cancer disease incidence worldwide has become a major public health issue. Manual histopathological analysis is a common diagnostic...
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Automated cervical cell segmentation using deep ensemble learning
BackgroundCervical cell segmentation is a fundamental step in automated cervical cancer cytology screening. The aim of this study was to develop and...
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Impact of brain segmentation methods on regional metabolism quantification in 18F-FDG PET/MR analysis
BackgroundAccurate analysis of quantitative PET data plays a crucial role in studying small, specific brain structures. The integration of PET and...
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OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification
Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational...
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Optimizing deep learning-based segmentation of densely packed cells using cell surface markers
BackgroundSpatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues,...
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Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database
Machine learning has been recently used especially in the medical field. In the diagnosis of serious diseases such as cancer, deep learning...
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Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review
BackgroundSegmentation tools continue to advance, evolving from manual contouring to deep learning. Researchers have utilized segmentation to study a...
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Multimodal Biomedical Image Segmentation using Multi-Dimensional U-Convolutional Neural Network
Deep learning recently achieved advancement in the segmentation of medical images. In this regard, U-Net is the most predominant deep neural network,...
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GA-UNet: A Lightweight Ghost and Attention U-Net for Medical Image Segmentation
U-Net has demonstrated strong performance in the field of medical image segmentation and has been adapted into various variants to cater to a wide...
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Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection
PurposeAccurate and automatic segmentation of basal ganglia from magnetic resonance (MR) images is important for diagnosis and treatment of various...
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Deep information-guided feature refinement network for colorectal gland segmentation
PurposeReliable quantification of colorectal histopathological images is based on the precise segmentation of glands but precise segmentation of...
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Alterations of thalamic nuclei volumes in patients with cluster headache
PurposeThis study aimed to compare the alterations of thalamic nuclei volumes and the intrinsic thalamic network in patients with cluster headache...
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Deep learning for histopathological segmentation of smooth muscle in the urinary bladder
BackgroundHistological assessment of smooth muscle is a critical step particularly in staging malignant tumors in various internal organs including...
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Threshold estimation based on local minima for nucleus and cytoplasm segmentation
BackgroundImage segmentation is the process of partitioning an image into separate objects or regions. It is an essential step in image processing to...
