A quantum-inspired classifier for clonogenic assay evaluations

High-Content and High-Throughput Clonogenic Survival Assay Using Fluorescence Barcoding

The Clonogenic Survival Assay (CSA) is a fundamental tool employed to assess cell survival and proliferative potential in cancer research. Despite its importance, CSA faces limitations, primarily its time- and labor-intensive nature and its binary output. To overcome these limitations and enhance CSA's utility, several approaches have been developed, focusing on increasing the throughput. However, achieving both high-content and high-throughput analyses simultaneously has remained a challenge. In this paper, we introduce LeGO-CSA, an extension of the classical CSA that employs the imaging of cell nuclei barcoded with fluorescent lentiviral gene ontology markers, enabling both high-content and high-throughput analysis. To validate our approach, we contrasted it with results from a classical assay and conducted a proof-of-concept screen of small-molecule inhibitors targeting various pathways relevant to cancer treatment. Notably, our results indicate that the classical CSA may underestimate clonogenicity and unveil intriguing aspects of clonal cell growth. We demonstrate the potential of LeGO-CSA to offer a robust approach for assessing cell survival and proliferation with enhanced precision and throughput, with promising implications for accelerating drug discovery and contributing to a more comprehensive understanding of cellular behavior in cancer.

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People's Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

Large-scale time-lapse scanning electron microscopy image mosaic using a smooth stitching strategy

Due to the trade-off between the field of view and resolution of various microscopes, obtaining a wide-view panoramic image through high-resolution image tiles is frequently encountered and demanded in numerous applications. Here, we propose an automatic image mosaic strategy for sequential 2D time-lapse scanning electron microscopy (SEM) images. This method can accurately compute pairwise translations among serial image tiles with indeterminate overlapping areas. The detection and matching of feature points are limited by geographical coordinates, thus avoiding accidental mismatching. Moreover, the nonlinear deformation of the mosaic part is also taken into account. A smooth stitching field is utilized to gradually transform the perspective transformation in overlapping regions into the linear transformation in non-overlapping regions. Experimental results demonstrate that better image stitching accuracy can be achieved compared with some other image mosaic algorithms. Such a method has potential applications in high-resolution large-area analysis using serial microscopy images. RESEARCH HIGHLIGHTS: An automatic image mosaic strategy for processing sequential scanning electron microscopy images is proposed. A smooth stitching field is applied in the image mosaic. Improved stitching accuracy is achieved compared with other conventional mosaic methods.

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning

Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. However, due to its high variability and human subjectivity, there is an insistent need to develop an efficient method for automatic image segmentation and classification. This article develops an automatic segmentation and classification framework based on artificial intelligence (AI) on the ANA images. The Otsu thresholding method and watershed segmentation algorithm are adopted to segment IIF images of cells. Moreover, multiple texture features such as scale-invariant feature transform (SIFT), local binary pattern (LBP), cooccurrence among adjacent LBPs (CoALBP), and rotation invariant cooccurrence among adjacent LBPs (RIC-LBP) are utilized. Firstly, this article adopts traditional machine learning methods such as support vector machine (SVM), k-nearest neighbor algorithm (KNN), and random forest (RF) and then uses ensemble classifier (ECLF) combined with soft voting rules to merge these machine learning methods for classification. The deep learning method InceptionResNetV2 is also utilized to train on the classification of cell images. Eventually, the best accuracy of 0.9269 on the Changsha dataset and 0.9635 on the ICPR 2016 dataset for the traditional methods is obtained by a combination of SIFT and RIC-LBP with the ECLF classifier, and the best accuracy obtained by the InceptionResNetV2 is 0.9465 and 0.9836 separately, which outperforms other schemes.

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

Multi Level Approach for Segmentation of Interstitial Lung Disease (ILD) Patterns Classification Based on Superpixel Processing and Fusion of K-Means Clusters: SPFKMC

During the COVID-19 pandemic, huge interstitial lung disease (ILD) lung images have been captured. It is high time to develop the efficient segmentation techniques utilized to separate the anatomical structures and ILD patterns for disease and infection level identification. The effectiveness of disease classification directly depends on the accuracy of initial stages like preprocessing and segmentation. This paper proposed a hybrid segmentation algorithm designed for ILD images by taking advantage of superpixel and K-means clustering approaches. Segmented superpixel images adapt the better irregular local and spatial neighborhoods that are helpful to improving the performance of K-means clustering-based ILD image segmentation. To overcome the limitations of multiclass belongings, semiadaptive wavelet-based fusion is applied over selected K-means clusters. The performance of the proposed SPFKMC was compared with that of 3-class Fuzzy C-Means clustering (FCM) and K-Means clustering in terms of accuracy, Jaccard similarity index, and Dice similarity coefficient. The SPFKMC algorithm gives an accuracy of 99.28%, DSC 98.72%, and JSI 97.87%. The proposed Fused Clustering gives better results as compared to traditional K-means clustering segmentation with wavelet-based fused cluster results.

WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms

White blood cell (WBC) morphology examination plays a crucial role in diagnosing many diseases. One of the most important steps in WBC morphology analysis is WBC image segmentation, which remains a challenging task. To address the problems of low segmentation accuracy caused by color similarity, uneven brightness, and irregular boundary between WBC regions and the background, a WBC image segmentation network based on U-Net combining residual networks and attention mechanism was proposed. Firstly, the ResNet50 residual block is used to form the main unit of the encoder structure, which helps to overcome the overfitting problem caused by a small number of training samples by improving the network's feature extraction capacity and loading the pretraining weight. Secondly, the SE module is added to the decoder structure to make the model pay more attention to useful features while suppressing useless ones. In addition, atrous convolution is utilized to recover full-resolution feature maps in the decoder structure to increase the receptive field of the convolution layer. Finally, network parameters are optimized using the Adam optimization technique in conjunction with the binary cross-entropy loss function. Experimental results on BCISC and LISC datasets show that the proposed approach has higher segmentation accuracy and robustness.

Leukocyte Segmentation Method Based on Adaptive Retinex Correction and U-Net

To address the issues of uneven illumination and inconspicuous leukocyte properties in the gathered cell pictures, a leukocyte segmentation method based on adaptive retinex correction and U-net was proposed. The procedure begins by processing a peripheral blood image to clearly distinguish leukocytes from other components in the image. The adaptive retinex correction, which is based on multiscale retinex with colour replication (MSRCR), redefines the colour recovery function by introducing Michelson contrast. Then, the image is trained with the U-net convolutional neural network, and the leukocyte segmentation is completed. The innovation is in the manner of processing peripheral blood images, which improves the accuracy of leukocyte segmentation. This study conducts experimental evaluations on the Cellavision, BCCD, and LISC datasets. The experimental results show that the method in this study is better than the current best method, and the segmentation accuracy rate reaches 98.87%.

Anticancer effects of carboxymethylated (1→3)(1→6)-β-D-glucan (botryosphaeran) on multicellular tumor spheroids of MCF-7 cells as a model of breast cancer

Breast cancer is the most common cancer worldwide among the female population. The fungal exopolysaccharide botryosphaeran is a (1→3)(1→6)-β-D-glucan with limited solubility in water that can be promoted through carboxymethylation. Thus, the aim of this study was to examine in-vitro anticancer effects of carboxymethylated-botryosphaeran (CM-BOT) on breast cancer MCF-7 cells cultivated in multicellular tumor spheroids (MCTS). CM-BOT (≥ 600 µ/ml) decreased the viability (resazurin assay) of MCF-7 grown in monolayers after 24 hr incubation. Although CM-BOT did not markedly alter viability of MCTS in the resazurin assay after 24, 48 or 72 hr, CM-BOT ≥ 600 µg/ml produced cell-death by apoptosis after 72 hr utilizing the triple staining assay and labeling dead cells with propidium iodide, which can also be visualized on the architecture of MCTS. CM-BOT (1000 µg/ml) inhibited cell proliferation, which resulted in MCTSs with smaller diameters than controls. CM-BOT at all concentrations examined decreased the ability of MCF-7 to form colonies and to migrate in the extracellular matrix. This is the first report using MCTS-architecture to study anti-tumor effects of β-glucans. Our findings are important in the search for compounds for use in breast cancer therapy, or as adjuvants in reducing the adverse effects of mammary tumor chemotherapy.

Improved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for the detection of Leukemia

The diagnosis of leukemia involves the detection of the abnormal characteristics of blood cells by a trained pathologist. Currently, this is done manually by observing the morphological characteristics of white blood cells in the microscopic images. Though there are some equipment- based and chemical-based tests available, the use and adaptation of the automated computer vision-based system is still an issue. There are certain software frameworks available in the literature; however, they are still not being adopted commercially. So there is a need for an automated and software- based framework for the detection of leukemia. In software-based detection, segmentation is the first critical stage that outputs the region of interest for further accurate diagnosis. Therefore, this paper explores an efficient and hybrid segmentation that proposes a more efficient and effective system for leukemia diagnosis. A very popular publicly available database, the acute lymphoblastic leukemia image database (ALL-IDB), is used in this research. First, the images are pre-processed and segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed. Different metrics are used for measuring the system performance. A comparative analysis of the proposed methodology is done with existing benchmarks methods. The proposed approach has proven to be better than earlier techniques with measuring parameters of PSNR and Similarity index. The result shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.