A Standardized Pipeline for Colon Nuclei Identification and Counting Challenge

2022-03-01 · Jijun Cheng, Xipeng Pan, Feihu Hou, Bingchao Zhao, Jiatai Lin, Zhenbing Liu, Zaiyi Liu, Chu Han

Nuclear segmentation and classification is an essential step for computational pathology. TIA lab from Warwick University organized a nuclear segmentation and classification challenge (CoNIC) for H&E stained histopathology images in colorectal cancer with two highly correlated tasks, nuclei segmentation and classification task and cellular composition task. There are a few obstacles we have to address in this challenge, 1) limited training samples, 2) color variation, 3) imbalanced annotations, 4) similar morphological appearance among classes. To deal with these challenges, we proposed a standardized pipeline for nuclear segmentation and classification by integrating several pluggable components. First, we built a GAN-based model to automatically generate pseudo images for data augmentation. Then we trained a self-supervised stain normalization model to solve the color variation problem. Next we constructed a baseline model HoVer-Net with cost-sensitive loss to encourage the model pay more attention on the minority classes. According to the results of the leaderboard, our proposed pipeline achieves 0.40665 mPQ+ (Rank 49th) and 0.62199 r2 (Rank 10th) in the preliminary test phase.

📄 PDF Abstract BibTeX arXiv:2203.00171

Code (0)

등록된 구현이 없습니다.

Tasks

Classification Data Augmentation Nuclear Segmentation Segmentation

MF-Hovernet: An Extension of Hovernet for Colon Nuclei Identification and Counting (CoNiC) Challenge

2022-03-04 · Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee

Nuclei Identification and Counting is the most important morphological feature of cancers, especially in the colon. Many deep learning-based methods have been proposed to deal with this problem. In this work, we construc…

CoNIC: Colon Nuclei Identification and Counting Challenge 2022

2021-11-29 · Simon Graham, Mostafa Jahanifar, Quoc Dang Vu, Giorgos Hadjigeorghiou 외

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models i…

Explainable Models Nuclear Segmentation

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

2022-03-01 · Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu 외

Classification Explainable Models Nuclear Segmentation Segmentation

Simultaneous Semantic and Instance Segmentation for Colon Nuclei Identification and Counting

2022-03-01 · Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Carola-Bibiane Schönlieb

We address the problem of automated nuclear segmentation, classification, and quantification from Haematoxylin and Eosin stained histology images, which is of great relevance for several downstream computational patholog…

Instance Segmentation Nuclear Segmentation Segmentation Semantic Segmentation

Panoptic segmentation with highly imbalanced semantic labels

2022-03-03 · Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Andrew Janowczyk 외

We describe here the panoptic segmentation method we devised for our participation in the CoNIC: Colon Nuclei Identification and Counting Challenge at ISBI 2022. Key features of our method are a weighted loss specificall…

Instance Segmentation Panoptic Segmentation Segmentation Semantic Segmentation