This is a simple framework for training neural networks to detect nodules in CT images. Training requires a json file (e.g. here) containing a list of CT images and the bounding boxes in each image. The model combines both CNN model and LSTM unit. The algorithm here is mainly refered to paper End-to-end people detection in crowded scenes.
The deep learning framewoek is based on TensorFlow(version 1.0.0) and some coding ideas are forked from the TensorBox project. Here I show heartfelt gratefulness. About nodule classfication method based on CNN transfer learning, you can refer to this paper.
Parsing the lidc XML annotation and dicom files，see pylung and this blog.
>>> import dicom
>>> f = dicom.read_file('000001.dcm')
>>> print f
(0008, 0005) Specific Character Set CS: 'ISO_IR 100'
(0008, 0008) Image Type CS: ['ORIGINAL', 'PRIMARY', 'AXIAL']
(0008, 0016) SOP Class UID UI: CT Image Storage
(0008, 0018) SOP Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.143451261327128179989900675595
(0008, 0020) Study Date DA: '20000101'
(0008, 0021) Series Date DA: '20000101'
(0008, 0022) Acquisition Date DA: '20000101'
(0008, 0023) Content Date DA: '20000101'
(0008, 0024) Overlay Date DA: '20000101'
(0008, 0025) Curve Date DA: '20000101'
(0008, 002a) Acquisition DateTime DT: '20000101'
(0008, 0030) Study Time TM: ''
(0008, 0032) Acquisition Time TM: ''
(0008, 0033) Content Time TM: ''
(0008, 0050) Accession Number SH: '2819497684894126'
(0008, 0060) Modality CS: 'CT'
(0008, 0070) Manufacturer LO: 'GE MEDICAL SYSTEMS'
(0008, 0090) Referring Physician Name PN: ''
(0008, 1090) Manufacturer Model Name LO: 'LightSpeed Plus'
(0008, 1155) Referenced SOP Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.675906998158803995297223798692
(0010, 0010) Patient Name PN: ''
(0010, 0020) Patient ID LO: 'LIDC-IDRI-0001'
(0010, 0030) Patient Birth Date DA: ''
(0010, 0040) Patient Sex CS: ''
(0010, 1010) Patient Age AS: ''
(0010, 21d0) Last Menstrual Date DA: '20000101'
(0012, 0062) Patient Identity Removed CS: 'YES'
(0012, 0063) De-identification Method LO: 'DCM:113100/113105/113107/113108/113109/113111'
(0013, 0010) Private Creator LO: 'CTP'
(0013, 1010) Private tag data LO: 'LIDC-IDRI'
(0013, 1013) Private tag data LO: '62796001'
(0018, 0010) Contrast/Bolus Agent LO: 'IV'
(0018, 0015) Body Part Examined CS: 'CHEST'
(0018, 0022) Scan Options CS: 'HELICAL MODE'
(0018, 0050) Slice Thickness DS: '2.500000'
(0018, 0060) KVP DS: '120'
(0018, 0090) Data Collection Diameter DS: '500.000000'
(0018, 1020) Software Version(s) LO: 'LightSpeedApps2.4.2_H2.4M5'
(0018, 1100) Reconstruction Diameter DS: '360.000000'
(0018, 1110) Distance Source to Detector DS: '949.075012'
(0018, 1111) Distance Source to Patient DS: '541.000000'
(0018, 1120) Gantry/Detector Tilt DS: '0.000000'
(0018, 1130) Table Height DS: '144.399994'
(0018, 1140) Rotation Direction CS: 'CW'
(0018, 1150) Exposure Time IS: '570'
(0018, 1151) X-Ray Tube Current IS: '400'
(0018, 1152) Exposure IS: '4684'
(0018, 1160) Filter Type SH: 'BODY FILTER'
(0018, 1170) Generator Power IS: '48000'
(0018, 1190) Focal Spot(s) DS: '1.200000'
(0018, 1210) Convolution Kernel SH: 'STANDARD'
(0018, 5100) Patient Position CS: 'FFS'
(0020, 000d) Study Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.298806137288633453246975630178
(0020, 000e) Series Instance UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.179049373636438705059720603192
(0020, 0010) Study ID SH: ''
(0020, 0011) Series Number IS: '3000566'
(0020, 0013) Instance Number IS: '80'
(0020, 0032) Image Position (Patient) DS: ['-166.000000', '-171.699997', '-207.500000']
(0020, 0037) Image Orientation (Patient) DS: ['1.000000', '0.000000', '0.000000', '0.000000', '1.000000', '0.000000']
(0020, 0052) Frame of Reference UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.229925374658226729607867499499
(0020, 1040) Position Reference Indicator LO: 'SN'
(0020, 1041) Slice Location DS: '-207.500000'
(0028, 0002) Samples per Pixel US: 1
(0028, 0004) Photometric Interpretation CS: 'MONOCHROME2'
(0028, 0010) Rows US: 512
(0028, 0011) Columns US: 512
(0028, 0030) Pixel Spacing DS: ['0.703125', '0.703125']
(0028, 0100) Bits Allocated US: 16
(0028, 0101) Bits Stored US: 16
(0028, 0102) High Bit US: 15
(0028, 0103) Pixel Representation US: 1
(0028, 0120) Pixel Padding Value US: 63536
(0028, 0303) Longitudinal Temporal Information M CS: 'MODIFIED'
(0028, 1050) Window Center DS: '-600'
(0028, 1051) Window Width DS: '1600'
(0028, 1052) Rescale Intercept DS: '-1024'
(0028, 1053) Rescale Slope DS: '1'
(0038, 0020) Admitting Date DA: '20000101'
(0040, 0002) Scheduled Procedure Step Start Date DA: '20000101'
(0040, 0004) Scheduled Procedure Step End Date DA: '20000101'
(0040, 0244) Performed Procedure Step Start Date DA: '20000101'
(0040, 2016) Placer Order Number / Imaging Servi LO: ''
(0040, 2017) Filler Order Number / Imaging Servi LO: ''
(0040, a075) Verifying Observer Name PN: 'Removed by CTP'
(0040, a123) Person Name PN: 'Removed by CTP'
(0040, a124) UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.335419887712224178340067932923
(0070, 0084) Content Creator's Name PN: ''
(0088, 0140) Storage Media File-set UID UI: 1.3.6.1.4.1.14519.5.2.1.6279.6001.211790042620307056609660772296
(7fe0, 0010) Pixel Data OW: Array of 524288 bytes
First, install TensorFlow from source or pip (NB: source installs currently break threading on 0.11). Then run the training script CNN_LSTM/run.sh.Note that running on your own dataset should require modifying the CNN_LSTM/hypes/\*.json file.
There are two options for evaluation, an ipython notebook and a python script.
- some test results
- precision-recall curve
- Anatomy map
- Parenchyma extraction
- Pulmonary nodules form
- [Dataset] The Lung Image Database Consortium image collection(LIDC-IDRI)
- [Paper] End-to-end people detection in crowded scenes
- [Paper] Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
- [Paper] An overview of classification algorithms for imbalanced datasets
- [Blog] LIDC-IDRI肺结节公开数据集Dicom和XML标注详解
- [Blog] 机器学习之分类性能度量指标 : ROC曲线、AUC值、正确率、召回率
- [简书] LIDC-IDRI肺结节Dicom数据集解析与总结
- [简书] LIDC-IDRI肺结节公开数据集Dicom和XML标注详解
- [简书] 医疗CT影像肺结节检测参考项目(附论文)
- [简书] 如何应用Python处理医学影像学中的DICOM信息
- [简书] CT图像肺结节识别算法调研 — CNN篇
- [简书] 吕乐：面向医学图像计算的深度学习与卷积神经网络（转）