Dataset Information

A Multi-Organ Nucleus Segmentation Challenge.

ABSTRACT: Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

SUBMITTER: Kumar N

PROVIDER: S-EPMC10439521 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A Multi-Organ Nucleus Segmentation Challenge.

Kumar Neeraj N Verma Ruchika R Anand Deepak D Zhou Yanning Y Onder Omer Fahri OF Tsougenis Efstratios E Chen Hao H Heng Pheng-Ann PA Li Jiahui J Hu Zhiqiang Z Wang Yunzhi Y Koohbanani Navid Alemi NA Jahanifar Mostafa M Tajeddin Neda Zamani NZ Gooya Ali A Rajpoot Nasir N Ren Xuhua X Zhou Sihang S Wang Qian Q Shen Dinggang D Yang Cheng-Kun CK Weng Chi-Hung CH Yu Wei-Hsiang WH Yeh Chao-Yuan CY Yang Shuang S Xu Shuoyu S Yeung Pak Hei PH Sun Peng P Mahbod Amirreza A Schaefer Gerald G Ellinger Isabella I Ecker Rupert R Smedby Orjan O Wang Chunliang C Chidester Benjamin B Ton That-Vinh TV Tran Minh-Triet MT Ma Jian J Do Minh N MN Graham Simon S Vu Quoc Dang QD Kwak Jin Tae JT Gunda Akshaykumar A Chunduri Raviteja R Hu Corey C Zhou Xiaoyang X Lotfi Dariush D Safdari Reza R Kascenas Antanas A O'Neil Alison A Eschweiler Dennis D Stegmaier Johannes J Cui Yanping Y Yin Baocai B Chen Kailin K Tian Xinmei X Gruening Philipp P Barth Erhardt E Arbel Elad E Remer Itay I Ben-Dor Amir A Sirazitdinova Ekaterina E Kohl Matthias M Braunewell Stefan S Li Yuexiang Y Xie Xinpeng X Shen Linlin L Ma Jun J Baksi Krishanu Das KD Khan Mohammad Azam MA Choo Jaegul J Colomer Adrian A Naranjo Valery V Pei Linmin L Iftekharuddin Khan M KM Roy Kaushiki K Bhattacharjee Debotosh D Pedraza Anibal A Bueno Maria Gloria MG Devanathan Sabarinathan S Radhakrishnan Saravanan S Koduganty Praveen P Wu Zihan Z Cai Guanyu G Liu Xiaojie X Wang Yuqin Y Sethi Amit A

IEEE transactions on medical imaging 20191023 5

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contest ...[more]

PMID: 31647422

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Project description:Objective. This work aims to develop an automated segmentation method for the prostate and its surrounding organs-at-risk in pelvic computed tomography to facilitate prostate radiation treatment planning.Approach. In this work, we propose a novel deep learning algorithm combining a U-shaped convolutional neural network (CNN) and vision transformer (VIT) for multi-organ (i.e. bladder, prostate, rectum, left and right femoral heads) segmentation in male pelvic CT images. The U-shaped model consists of three components: a CNN-based encoder for local feature extraction, a token-based VIT for capturing global dependencies from the CNN features, and a CNN-based decoder for predicting the segmentation outcome from the VIT's output. The novelty of our network is a token-based multi-head self-attention mechanism used in the transformer, which encourages long-range dependencies and forwards informative high-resolution feature maps from the encoder to the decoder. In addition, a knowledge distillation strategy is deployed to further enhance the learning capability of the proposed network.Main results. We evaluated the network using: (1) a dataset collected from 94 patients with prostate cancer; (2) and a public dataset CT-ORG. A quantitative evaluation of the proposed network's performance was performed on each organ based on (1) volume similarity between the segmented contours and ground truth using Dice score, segmentation sensitivity, and precision, (2) surface similarity evaluated by Hausdorff distance (HD), mean surface distance (MSD) and residual mean square distance (RMS), (3) and percentage volume difference (PVD). The performance was then compared against other state-of-art methods. Average volume similarity measures obtained by the network overall organs were Dice score = 0.91, sensitivity = 0.90, precision = 0.92, average surface similarities were HD = 3.78 mm, MSD = 1.24 mm, RMS = 2.03 mm; average percentage volume difference was PVD = 9.9% on the first dataset. The network also obtained Dice score = 0.93, sensitivity = 0.93, precision = 0.93, average surface similarities were HD = 5.82 mm, MSD = 1.16 mm, RMS = 1.24 mm; average percentage volume difference was PVD = 6.6% on the CT-ORG dataset.Significance. In summary, we propose a token-based transformer network with knowledge distillation for multi-organ segmentation using CT images. This method provides accurate and reliable segmentation results for each organ using CT imaging, facilitating the prostate radiation clinical workflow.

Project description:BackgroundLight microscopy is a widely used technique in cell biology due to its satisfactory resolution for cellular structure analysis, prevalent availability of fluorescent probes for staining, and compatibility for the dynamic analysis of live cells. However, the segmentation of cells and nuclei from microscopic images is not a straightforward process because it has several challenges such as high variation in morphology and shape, the presence of noise and diverse contrast in backgrounds, clustering or overlapping nature of cells. Dealing with these challenges and facilitating more reliable analysis necessitates the implementation of computer-aided methods that leverage image processing techniques and deep learning algorithms. The major goal of this study is to propose a model, for instance segmentation of cells and nuclei, applying the most cutting-edge deep learning techniques.MethodsA fine-tuned You Only Look at Once version 9 extended (YOLOv9-E) model is initially applied as a prompt generator to generate bounding box prompts. Using the generated prompts, a pre-trained segment anything model (SAM) is subsequently applied through zero-short inferencing to produce raw segmentation masks. These segmentation masks are then refined using non-max suppression and simple image processing methods such as image addition and morphological processing. The proposed method is developed and evaluated using an open-sourced dataset called Expert Visual Cell Annotation (EVICAN), which is relatively large and contains 4,738 microscopy images extracted from cross organs using different protocols.ResultsBased on the evaluation results on three different levels of EVICAN test sets, the proposed method demonstrates noticeable performances showing average mAP50 [mean average precision at intersection over union (IoU) =0.50] scores of 96.25, 95.05, and 94.18 for cell segmentation, and 68.04, 54.66, and 38.29 for nucleus segmentation on easy, medium, and difficult test sets, respectively.ConclusionsOur proposed method for instance segmentation of cells and nuclei provided favorable performance compared to the existing methods in literature, indicating its potential utility as an assistive tool for cell culture experts, facilitating prompt and reliable analysis.

Project description:The abdomen houses multiple vital organs, which are associated with various diseases posing significant risks to human health. Early detection of abdominal organ conditions allows for timely intervention and treatment, preventing deterioration of patients' health. Segmenting abdominal organs aids physicians in more accurately diagnosing organ lesions. However, the anatomical structures of abdominal organs are relatively complex, with organs overlapping each other, sharing similar features, thereby presenting challenges for segmentation tasks. In real medical scenarios, models must demonstrate real-time and low-latency features, necessitating an improvement in segmentation accuracy while minimizing the number of parameters. Researchers have developed various methods for abdominal organ segmentation, ranging from convolutional neural networks (CNNs) to Transformers. However, these methods often encounter difficulties in accurately identifying organ segmentation boundaries. MetaFormer abstracts the framework of Transformers, excluding the multi-head Self-Attention, offering a new perspective for solving computer vision problems and overcoming the limitations of Vision Transformers and CNN backbone networks. To further enhance segmentation effectiveness, we propose a U-shaped network, integrating SEFormer and depthwise cascaded upsampling (dCUP) as the encoder and decoder, respectively, into the UNet structure, named SEF-UNet. SEFormer combines Squeeze-and-Excitation modules with depthwise separable convolutions, instantiating the MetaFormer framework, enhancing the capture of local details and texture information, thereby improving edge segmentation accuracy. dCUP further integrates shallow and deep information layers during the upsampling process. Our model significantly improves segmentation accuracy while reducing the parameter count and exhibits superior performance in segmenting organ edges that overlap each other, thereby offering potential deployment in real medical scenarios.

Dataset Information

A Multi-Organ Nucleus Segmentation Challenge.

Publications

A Multi-Organ Nucleus Segmentation Challenge.

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