Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟭𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 Shanghai AI Lab Research and West China hospital has collaboratively published 𝗪𝗢𝗥𝗗: 𝗥𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 𝗢𝗿𝗴𝗮𝗻𝘀 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗪𝗵𝗼𝗹𝗲 𝗔𝗯𝗱𝗼𝗺𝗶𝗻𝗮𝗹 𝗥𝗲𝗴𝗶𝗼𝗻 Follow me for a similar post: 🇮🇳 Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗪𝗢𝗥𝗗: 𝗥𝗲𝘃𝗶𝘀𝗶𝘁𝗶𝗻𝗴 𝗢𝗿𝗴𝗮𝗻𝘀 𝗦𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗪𝗵𝗼𝗹𝗲 𝗔𝗯𝗱𝗼𝗺𝗶𝗻𝗮𝗹 𝗥𝗲𝗴𝗶𝗼𝗻 🔸 This paper is published arxiv 2021. 🔸 Abdominal organ segmentation is a very fundamental and important task in abdominal disease diagnosis, cancer treatment, and radiotherapy planning. As accurately segmented organs can provide pieces of valuable information for the clinical diagnosis and follow-ups like organ size, location, boundary state, and spatial relationship of multi- ple organs, etc 🔸 In contrast, our WORD was collected from a radiation therapy center and annotated by one senior oncologist (with 7 years experience) and then checked by an expert of oncology (more than 20 years experience). ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Whole abdominal organs segmentation plays an impor- tant role in abdomen lesion diagnosis, radiotherapy plan- ning, and follow-up. However, delineating all abdominal organs by oncologists manually is time-consuming and very expensive. 🔸Recently, deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training. Although many efforts in this task, there are still few large image datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation. 🔸In this work, we establish a large-scale Whole abdominal ORgans Dataset (WORD) for algorithms research and clinical applications development. This dataset contains 150 abdominal CT volumes (30495 slices) and each volume has 16 organs with fine pixel-level annotations and scribble-based sparse annotation, which may be the largest dataset with whole abdominal organs annotation. 🔸Several state-of-the-art segmentation methods are evaluated on this dataset. And, we also invited clinical oncologists to re- vise the model predictions to measure the gap between the deep learning method and real oncologists. We further in- troduce and evaluate a new scribble-based weakly super- vised segmentation on this dataset. The work provided a new benchmark for the abdominal multi-organ segmentation task and these experiments can serve as the baseline for future research and clinical application development. #computervision #artificialintelligence #technology