Segmentation and counting of multiple myeloma cells using IEMD based deep neural network

Rasal, Tushar; Veerakumar, T.; Subudhi, Bidyadhar; Esakkirajan, S.

doi:10.1016/j.leukres.2022.106950

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…Moreover, the available datasets utilized in prior studies were generated using high-quality imaging equipment attached to microscopes. In contrast, the present dataset was produced using equipment commonly available in laboratories that cater to the Brazilian Public Health System 22 , 23 , 28 , 32 . From this standpoint, the cell-type classification provided by the current dataset enables the identification of other nucleated cells, which is a critical component of various diagnostic procedures and has recently been the focus of several studies aimed at automating this time-intensive process.…”

Section: Discussionmentioning

confidence: 99%

“…Prior projects using smaller datasets have demonstrated promising results, albeit limited to plasma cell identification. However, these datasets do not furnish the necessary information for percentage counting of plasma cells, which is vital in identifying multiple myeloma 22 , 23 , 28 , 32 .…”

Section: Discussionmentioning

confidence: 99%

“…However, the images derived from this dataset differ significantly from ours, since each segmented image comprises only a single cell positioned at the origin. The second one, published in 2022, presents an approach combining DNN and a decomposition method to segment plasma cells into nuclei and membranes 23 . The dataset considered by the authors contains 298 images with high resolution.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

Andrade,

Ferreira,

Alencar

et al. 2024

Sci Rep

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Multiple Myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells within the bone marrow. Diagnosing MM presents considerable challenges, involving the identification of plasma cells in cytology examinations on hematological slides. At present, this is still a time-consuming manual task and has high labor costs. These challenges have adverse implications, which rely heavily on medical professionals’ expertise and experience. To tackle these challenges, we present an investigation using Artificial Intelligence, specifically a Machine Learning analysis of hematological slides with a Deep Neural Network (DNN), to support specialists during the process of diagnosing MM. In this sense, the contribution of this study is twofold: in addition to the trained model to diagnose MM, we also make available to the community a fully-curated hematological slide dataset with thousands of images of plasma cells. Taken together, the setup we established here is a framework that researchers and hospitals with limited resources can promptly use. Our contributions provide practical results that have been directly applied in the public health system in Brazil. Given the open-source nature of the project, we anticipate it will be used and extended to diagnose other malignancies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

Andrade,

Ferreira,

Alencar

et al. 2024

Sci Rep

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“…The routes comprise the threshold method, declumping clustered objects and dividing the lines of clustered objects. Cell counting highly relies on thresholding, as also mentioned by others [25][26][27], with the threshold helping in the conversion of images into binary form, thereby making it easier to distinguish the object's edges. All routes 34, 35, 61 and 62 utilized adaptive as their threshold strategy and 'Shape' and 'Propagate' as methods of declumping the clustered objects and dividing the clustered objects, respectively.…”

Section: Discussionmentioning

confidence: 99%

Automated Cell Counting using Image Processing

Hassan,

Suhaimi,

Bilad

et al. 2023

IJC

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Manual cell counting using Hemocytometer is commonly used to quantify cells, as it is an inexpensive and versatile method. However, it is labour-intensive, tedious, and time-consuming. On the other hand, most automated cell counting methods are expensive and require experts to operate. Thus, the use of image analysis software allows one to access low-cost but robust automated cell counting. This study explores the advanced setting of image processing software to obtain routes with the highest counting accuracy. The results show the effectiveness of advanced settings in CellProfiler for counting cells from synthetic images. Two routes were found to give the highest performance, with average image and cell accuracies of 85% and 99.8%, respectively, and the highest F1 score of 0.83. However, the two routes were unable to correctly determine the exact number of cells on the histology images, albeit giving a respectable cell accuracy of 79.6%. Further investigation has shown that CellProfiler is able to correctly identify the bulk of the cells within the histology images. Good image quality with high focus and less blur was identified as the key to successful image-based cell counting. To further enhance the accuracy, other modules can be included to further segment an object hence improving the number of objects identified. Future work can focus on evaluating the robustness of the routes by comparing them with other methods and validating with the manual cell counting method.

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AAMR-FCN myeloma cancer net: Adaptive and attention-based mask R-FCN for diagnosing myeloma cancer using cell microscopic images with hybrid heuristic strategy

Shinu,

Pamela,

Glan Devadhas

et al. 2025

Biomedical Signal Processing and Control

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Segmentation and counting of multiple myeloma cells using IEMD based deep neural network

Cited by 5 publications

References 20 publications

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

Automated Cell Counting using Image Processing

AAMR-FCN myeloma cancer net: Adaptive and attention-based mask R-FCN for diagnosing myeloma cancer using cell microscopic images with hybrid heuristic strategy

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