Threshold estimation based on local minima for nucleus and cytoplasm segmentation

Mayala, Simeon; Haugsøen, Jonas Bull

doi:10.1186/s12880-022-00801-w

Cited by 7 publications

(6 citation statements)

References 34 publications

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“…Intensity and lifetime images of mCherry, thresholded to a predefined lifetime range of 800-1500 ps to reduce non-specific signal, were passed in as initial inputs to the pipeline. Initial global and local thresholding of intensity images through a series of standard thresholding methods – otsu, yen, min, triangle, local, and their combinations – was used to differentiate foreground vs. background ( 50 , 51 , 52 ). Next, a left-merge intersection was performed to combine the thresholded intensity image and the range-limited lifetime image.…”

Section: Methodsmentioning

confidence: 99%

Single cell metabolic imaging of tumor and immune cells in vivo in melanoma bearing mice

et al. 2023

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IntroductionMetabolic reprogramming of cancer and immune cells occurs during tumorigenesis and has a significant impact on cancer progression. Unfortunately, current techniques to measure tumor and immune cell metabolism require sample destruction and/or cell isolations that remove the spatial context. Two-photon fluorescence lifetime imaging microscopy (FLIM) of the autofluorescent metabolic coenzymes nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) and flavin adenine dinucleotide (FAD) provides in vivo images of cell metabolism at a single cell level.MethodsHere, we report an immunocompetent mCherry reporter mouse model for immune cells that express CD4 either during differentiation or CD4 and/or CD8 in their mature state and perform in vivo imaging of immune and cancer cells within a syngeneic B78 melanoma model. We also report an algorithm for single cell segmentation of mCherry-expressing immune cells within in vivo images.ResultsWe found that immune cells within B78 tumors exhibited decreased FAD mean lifetime and an increased proportion of bound FAD compared to immune cells within spleens. Tumor infiltrating immune cell size also increased compared to immune cells from spleens. These changes are consistent with a shift towards increased activation and proliferation in tumor infiltrating immune cells compared to immune cells from spleens. Tumor infiltrating immune cells exhibited increased FAD mean lifetime and increased protein-bound FAD lifetime compared to B78 tumor cells within the same tumor. Single cell metabolic heterogeneity was observed in both immune and tumor cells in vivo.DiscussionThis approach can be used to monitor single cell metabolic heterogeneity in tumor cells and immune cells to study promising treatments for cancer in the native in vivo context.

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Section: Methodsmentioning

confidence: 99%

Single cell metabolic imaging of tumor and immune cells in vivo in melanoma bearing mice

et al. 2023

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“…This threshold value limits the local minima, in which only the voxel contrast value is greater than the min-threshold (δmin) are considered for background separation to optimize the computational depth. Threshold approximation method defined by the Mayala and Haugsøen [18] for nucleus segmentation is adopted in our research to determine the minthreshold (δmin) value. Based on the image pixel intensity and their frequencies approximation from histograms, the minthreshold is evaluated in this research.…”

Section: D-cu-net and Kidney Tumor Segmentationmentioning

confidence: 99%

Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation

Parvathi¹,

Chandana²,

Harikiran³

2023

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Due to the impact and importance of the kidney objects in human body, the kidney tumor analysis from three dimensional CT and MRI medical images becomes a pivotal research topic, which helps in diagnosing the kidney diseases like kidney stones, polycystic and kidney tumors etc. In deep learning, U-Net became a prominent and reliable solution for kidney image analysis and objects segmentation process. Although several research works were focused on kidney object detection and tumor segmentation from medical images, they are suffering from some intrinsic limitations due to: variance in network depths, enforced feature fusion, segmentation errors and inaccuracy. In order to address these limitations in kidney tumor segmentation process, in this paper we proposed the 3D-CU-Net model for kidney tumor segmentation, which is a custom variant of the U-Net. In 3D-CU-Net, the encoder-decoder network model is unified to tolerate the depth invariance issues, while training various input images with the same model. Completely connected dense skip connections are designed at each layer of 3D-CU-Net, to control the enforced feature fusion and to extract the crucial features. An integrated loss function is designed with Binary Cross Entropy (BCE) and Soft-Dice Coefficient (SDC) to mitigate the segmentation errors and inaccuracy. Experiments on TCGA-KIRC dataset with 3D-CU-NET recorded the high accuracy in kidney tumor segmentation with mIoU (91.21%) and mDSC (92.69%).

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“…Leukocytes, commonly known as white blood cells, are a vital component of the immune system and play a pivotal role in protecting the body against infections 1 . Their primary function is to identify and combat pathogens, including bacteria, viruses, and fungi, using various mechanisms such as phagocytosis, antibody production, and the release of chemical signals to attract other immune cells 2–5 . Leukocytes are further categorized into different subtypes, including neutrophils, lymphocytes, monocytes, eosinophils, and basophils 6 .…”

Section: Introductionmentioning

confidence: 99%

CAB‐Net: Convolutional attention BLSTM network for enhanced leukocyte classification

Zonyfar,

Kim

2024

Int J Imaging Syst Tech

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In recent years, deep learning techniques have been increasingly utilized to automate and reduce human errors in leukocyte classification. However, reliability and accuracy issues remain a challenge. This paper proposes CAB‐Net, an end‐to‐end deep learning‐based model that integrates a convolutional neural network with an attention mechanism and BLSTM. The model uses a stack of convolutional and pooling layers to extract initial features from input images, which are then fed into the attention block to generate class‐specific discriminative features and BLSTM employed to model deterministic class correlations between highlighted discriminative features and labels. Experimental results show that CAB‐Net outperforms state‐of‐the‐art methods in term of accuracy, precision, recall, F1‐Score, and AUC‐ROC. In addition, the CAB‐Net model demonstrates good performance even with low‐resolution images. Consequently, CAB‐Net can be considered as the current state‐of‐the‐art prediction model for improving leukocyte classification in clinical environments.

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Threshold estimation based on local minima for nucleus and cytoplasm segmentation

Cited by 7 publications

References 34 publications

Single cell metabolic imaging of tumor and immune cells in vivo in melanoma bearing mice

Single cell metabolic imaging of tumor and immune cells in vivo in melanoma bearing mice

Depth Invariant 3D-CU-Net Model with Completely Connected Dense Skip Networks for MRI Kidney Tumor Segmentation

CAB‐Net: Convolutional attention BLSTM network for enhanced leukocyte classification

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