PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma

Gupta, Anubha; Mallick, Pramit; Sharma, Ojaswa; Gupta, Ritu; Duggal, Rahul

doi:10.1371/journal.pone.0207908

Cited by 31 publications

(28 citation statements)

References 32 publications

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“…However, in the long-term follow-up, there was no significant difference in the mortality rate between the two types of stents [ 21 ]. Duggalb et al, reported that there was a statistically significant difference in unadjusted mortality rates between the BMS (5%) and DES (3.8%) [ 26 ]. Also, in the study by Dieguez et al [ 27 ], the rate of all-cause mortality in patients treated with DES (6.5%) was significantly lower than that of patients treated with BMS (12.2%) ( P = 0.049).…”

Section: Discussionmentioning

confidence: 99%

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Farhadian

Karsidani

Mozayanimonfared

et al. 2021

BMC Cardiovasc Disord

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Background Due to the limited number of studies with long term follow-up of patients undergoing Percutaneous Coronary Intervention (PCI), we investigated the occurrence of Major Adverse Cardiac and Cerebrovascular Events (MACCE) during 10 years of follow-up after coronary angioplasty using Random Survival Forest (RSF) and Cox proportional hazards models. Methods The current retrospective cohort study was performed on 220 patients (69 women and 151 men) undergoing coronary angioplasty from March 2009 to March 2012 in Farchshian Medical Center in Hamadan city, Iran. Survival time (month) as the response variable was considered from the date of angioplasty to the main endpoint or the end of the follow-up period (September 2019). To identify the factors influencing the occurrence of MACCE, the performance of Cox and RSF models were investigated in terms of C index, Integrated Brier Score (IBS) and prediction error criteria. Results Ninety-six patients (43.7%) experienced MACCE by the end of the follow-up period, and the median survival time was estimated to be 98 months. Survival decreased from 99% during the first year to 39% at 10 years' follow-up. By applying the Cox model, the predictors were identified as follows: age (HR = 1.03, 95% CI 1.01–1.05), diabetes (HR = 2.17, 95% CI 1.29–3.66), smoking (HR = 2.41, 95% CI 1.46–3.98), and stent length (HR = 1.74, 95% CI 1.11–2.75). The predictive performance was slightly better by the RSF model (IBS of 0.124 vs. 0.135, C index of 0.648 vs. 0.626 and out-of-bag error rate of 0.352 vs. 0.374 for RSF). In addition to age, diabetes, smoking, and stent length, RSF also included coronary artery disease (acute or chronic) and hyperlipidemia as the most important variables. Conclusion Machine-learning prediction models such as RSF showed better performance than the Cox proportional hazards model for the prediction of MACCE during long-term follow-up after PCI.

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Section: Discussionmentioning

confidence: 99%

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Farhadian

Karsidani

Mozayanimonfared

et al. 2021

BMC Cardiovasc Disord

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“…The microscopic images of multiple myeloma cells applied in this research were obtained from the TCIA_SegPC_dataset [ 4 , 9 , 10 ]. It is a publicly released dataset for academic and research purposes after the SegPC2021 challenge.…”

Section: Methodsmentioning

confidence: 99%

“…We try to remove unstained cells because they can hinder the learning process of Mask R-CNN. A study by Gupta [ 4 ] performed a detailed analysis of different objects from the input cells images. They emphasized four different objects (ROIs): the nucleus of plasma cells, cytoplasm of plasma cells, unstained cells, and background.…”

Section: Methodsmentioning

confidence: 99%

“…Many research papers focused on the automatic detection and segmentation of cells from microscopic images, but, to the best of our knowledge, there are inconsiderable numbers of papers that emphasize myeloma cells. The automatic detection and segmentation of plasma cell segmentation for myeloma diagnosis is very challenging and complicated due to the following reasons [ 4 ]: The number of nucleus and cytoplasm may vary from one cell to another; The boundary of the plasma cells is fuzzy because the cytoplasm of the cell and the background of the image have a similar visual appearance; In some situations, cells may be isolated as single cells, but sometimes they may be in clusters; If cells are in clusters, they may be in three different conditions: (i) Nuclei of cells are touching; (ii) Cytoplasm of cells are touching; and (iii) Cytoplasm of one cell is touching the nucleus of other cells. In such cases, the computer-aided segmentation process will be more challenging because the cytoplasm and nucleus expose different colors; In some situations, it is possible to have an unstained cell (for example, a red blood cell) underneath the cell of interest (MM plasma cell).…”

Section: Introductionmentioning

confidence: 99%

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Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

Paing

Sento

Bui

et al. 2022

Entropy

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Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.

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“…The algorithm developed is based on Otsu's thresholding. Gupta [15] in his research, he presented the PCSeg Tool for segmenting blood plasma cell images. The algorithm that he uses is pre-processing the existing input images by removing all information that is not needed so that all that remains is the region that does provide information about the blood plasma cells only.…”

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confidence: 99%

Preliminary process in blast cell morphology identification based on image segmentation methods

Supriyanti

Wibowo

Firmanda

et al. 2020

IJECE

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The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

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PCSeg: Color model driven probabilistic multiphase level set based tool for plasma cell segmentation in multiple myeloma

Cited by 31 publications

References 32 publications

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Risk factors associated with major adverse cardiac and cerebrovascular events following percutaneous coronary intervention: a 10-year follow-up comparing random survival forest and Cox proportional-hazards model

Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

Preliminary process in blast cell morphology identification based on image segmentation methods

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