Pediatric Sarcoma Segmentation using Deep Learning

肉瘤 医学 分割 骨肉瘤 横纹肌肉瘤 核医学 放射科 深度学习 人工智能 计算机科学 病理
作者
Louise à ̃rum,Kirstine Banke,Lise Borgwardt,Adam E. Hansen,Liselotte Hà ̧jgaard,Flemming Littrup Andersen,Claes Nøhr Ladefoged
出处
期刊:The Journal of Nuclear Medicine [Society of Nuclear Medicine and Molecular Imaging]
卷期号:60: 1208-1208
摘要

1208 Introduction: Pediatric sarcomas are a rare and complex cancer type consisting of several subtypes with different characteristics. Varying response to treatment within the same subtype has been observed clinically, calling for large systematic studies. Here, deep learning is used to implement an algorithm for automatic segmentation of pediatric sarcomas in PET/CT scans, with the final aim of paving the way for stratification studies. Methods: This retrospective study included 58 pediatric patients (32/26 m/f) age 6 mo. to 18 yo. with proven sarcomas and whole body 18F-FDG PET/CT. The main diagnoses were Rhabdomyosarcoma (RMS), Osteogenic sarcoma (OS) or Ewing sarcoma (ES). All images were anonymized. PET where acquired 60 min p.i of 3 MBq/kg body weight with a scan time of 3 min/bed. The CT were acquired in diagnostic quality with IV contrast. Additional 51 verified soft tissue sarcoma PET/CT data was obtained from The Cancer Imaging Archive (TCIA). These subjects had PET/CT with similar parameters. All tumors were delineated manually by a nuclear medicine specialist and served as gold standard for segmentation. A deep learning network that predict segmentation of sarcomas on the PET/CT data was trained using a model adapted from U-Net [1]. The model is build up by four encoding blocks, a base block, and four decoding blocks. For the convolution blocks maxpool is used for downscaling, ReLu as activation function, dropout and batch normalization is applied. Generalized Dice overlap was used as loss function. Before the final output, softmax activation is applied mapping each output pixel to a probability (tumor/background). The input is six channels; 3 consecutive 2D slices of PET data plus 3 consecutive 2D slices of CT data all with matrix size of 400 x 400. The output is two 400 x 400 images, one for each class (tumor/background) representing the center slice of the input slices. Data are presented to the network with random slice selection. During training an Adam optimizer is used with default values for β1= 0.9 and β2 = 0.999. The weights are initialized using He-initializer. The network is regularized by L2-weight regularization with a λ = 0.1 and by data augmentation using flip and rotation of the input. The model was evaluated by K-fold cross validation with K=5. Results: Hyperparameter search were performed and batch size=8, Learning rate= 1e-02, dropout rate as 0.1, 0.2, 0.3 at block level 1,2 and 3 respectively were found to be optimal. For tumor segmentation a voxel wise precision and sensitivity was found at 0.71/0.54 for Thorax, 0.71/0.39 for extremities, 0.52/0.38 for abdomen. For the head, neck and brain region the segmentation performed poorly mainly due to the high uptake in the brain, furthermore high FDG uptake in bladder and kidney also contributes to erroneous segmentations reflected by the precision and sensitivity scores. Discussion: A deep leaning network was trained to segment sarcomas on pediatric patients. Sarcomas are a very heterogenous class of tumors with large variations in size, shape and metabolism. This showed to be a challenging problem. Nevertheless, the network was able to accurately segment quite complex tumors (See figure, automatic segmentation in red outline) while in other cases it would fail. We attribute this to the limited number of training cases and hypothesize that an increasing number of training samples would improve the network. References: [1] Ronneberger et al, U-Net: Convolutional Networks for Biomedical Image Segmentation in Medical Image Computing and Computer-Assisted Intervention (MICCAI) 9351 (2015).

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
暮光微凉完成签到,获得积分10
刚刚
刚刚
3秒前
无花果应助Zymiao采纳,获得10
3秒前
4秒前
小王爱学习给小王爱学习的求助进行了留言
5秒前
Liu关闭了Liu文献求助
6秒前
7秒前
Ni发布了新的文献求助10
7秒前
7秒前
kimiweiwei完成签到,获得积分10
9秒前
1234发布了新的文献求助10
9秒前
wangying完成签到,获得积分10
10秒前
狗子完成签到 ,获得积分10
11秒前
身强力壮运气好完成签到,获得积分10
11秒前
Miki发布了新的文献求助10
11秒前
Cici发布了新的文献求助10
12秒前
抽屉里的砖头完成签到,获得积分10
12秒前
黑虎发布了新的文献求助10
13秒前
15秒前
Broccoli发布了新的文献求助10
15秒前
李健的粉丝团团长应助CFF采纳,获得10
16秒前
脑洞疼应助超帅的又槐采纳,获得10
16秒前
17秒前
完美的大米完成签到,获得积分10
18秒前
果冻橙发布了新的文献求助10
19秒前
iui飞发布了新的文献求助10
20秒前
大头粽完成签到,获得积分10
20秒前
量子星尘发布了新的文献求助10
20秒前
monica-q完成签到,获得积分10
20秒前
花花猪1989发布了新的文献求助20
21秒前
灿烂完成签到,获得积分10
23秒前
23秒前
23秒前
24秒前
24秒前
水门发布了新的文献求助30
26秒前
lulu加油发布了新的文献求助30
27秒前
28秒前
29秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3969940
求助须知:如何正确求助?哪些是违规求助? 3514642
关于积分的说明 11175298
捐赠科研通 3249947
什么是DOI,文献DOI怎么找? 1795178
邀请新用户注册赠送积分活动 875617
科研通“疑难数据库(出版商)”最低求助积分说明 804891