A semiautomatic CT-based ensemble segmentation of lung tumors: Comparison with oncologists’ delineations and with the surgical specimen

AbstractPurposeTo assess the clinical relevance of a semiautomatic CT-based ensemble segmentation method, by comparing it to pathology and to CT/PET manual delineations by five independent radiation oncologists in non-small cell lung cancer (NSCLC).Materials and methodsFor 20 NSCLC patients (stages Ib–IIIb) the primary tumor was delineated manually on CT/PET scans by five independent radiation oncologists and segmented using a CT based semi-automatic tool. Tumor volume and overlap fractions between manual and semiautomatic-segmented volumes were compared. All measurements were correlated with the maximal diameter on macroscopic examination of the surgical specimen. Imaging data are available on www.cancerdata.org.ResultsHigh overlap fractions were observed between the semi-automatically segmented volumes and the intersection (92.5±9.0, mean±SD) and union (94.2±6.8) of the manual delineations. No statistically significant differences in tumor volume were observed between the semiautomatic segmentation (71.4±83.2cm3, mean±SD) and manual delineations (81.9±94.1cm3; p=0.57). The maximal tumor diameter of the semiautomatic-segmented tumor correlated strongly with the macroscopic diameter of the primary tumor (r=0.96).ConclusionsSemiautomatic segmentation of the primary tumor on CT demonstrated high agreement with CT/PET manual delineations and strongly correlated with the macroscopic diameter considered as the “gold standard”. This method may be used routinely in clinical practice and could be employed as a starting point for treatment planning, target definition in multi-center clinical trials or for high throughput data mining research. This method is particularly suitable for peripherally located tumors

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

p-i-n heterojunctions with BiFeO3 perovskite nanoparticles and p- and n-type oxides: photovoltaic properties.

We formed p-i-n heterojunctions based on a thin film of BiFeO3 nanoparticles. The perovskite acting as an intrinsic semiconductor was sandwiched between a p-type and an n-type oxide semiconductor as hole- and electron-collecting layer, respectively, making the heterojunction act as an all-inorganic oxide p-i-n device. We have characterized the perovskite and carrier collecting materials, such as NiO and MoO3 nanoparticles as p-type materials and ZnO nanoparticles as the n-type material, with scanning tunneling spectroscopy; from the spectrum of the density of states, we could locate the band edges to infer the nature of the active semiconductor materials. The energy level diagram of p-i-n heterojunctions showed that type-II band alignment formed at the p-i and i-n interfaces, favoring carrier separation at both of them. We have compared the photovoltaic properties of the perovskite in p-i-n heterojunctions and also in p-i and i-n junctions. From current-voltage characteristics and impedance spectroscopy, we have observed that two depletion regions were formed at the p-i and i-n interfaces of a p-i-n heterojunction. The two depletion regions operative at p-i-n heterojunctions have yielded better photovoltaic properties as compared to devices having one depletion region in the p-i or the i-n junction. The results evidenced photovoltaic devices based on all-inorganic oxide, nontoxic, and perovskite materials

Radiomics for Response Assessment after Stereotactic Radiotherapy for Lung Cancer

Stereotactic ablative radiotherapy (SABR) is a guideline-specified treatment option for patients with early stage non-small cell lung cancer. After treatment, patients are followed up regularly with computed tomography (CT) imaging to determine treatment response. However, benign radiographic changes to the lung known as radiation-induced lung injury (RILI) frequently occur. Due to the large doses delivered with SABR, these changes can mimic the appearance of a recurring tumour and confound response assessment. The objective of this work was to evaluate the accuracy of radiomics, for prediction of eventual local recurrence based on CT images acquired within 6 months of treatment. A semi-automatic decision support system was developed to segment and sample regions of common post-SABR changes, extract radiomic features and classify images as local recurrence or benign injury. Physician ability to detect timely local recurrence was also measured on CT imaging, and compared with that of the radiomics tool. Within 6 months post-SABR, physicians assessed the majority of images as no recurrence and had an overall lower accuracy compared to the radiomics system. These results suggest that radiomics can detect early changes associated with local recurrence that are not typically considered by physicians. These appearances detected by radiomics may be early indicators of the promotion and progression to local recurrence. This has the potential to lead to a clinically useful computer-aided decision support tool based on routinely acquired CT imaging, which could lead to earlier salvage opportunities for patients with recurrence and fewer invasive investigations of patients with only benign injury

Volumetric CT-based segmentation of NSCLC using 3D-Slicer

Accurate volumetric assessment in non-small cell lung cancer (NSCLC) is critical for adequately informing treatments. In this study we assessed the clinical relevance of a semiautomatic computed tomography (CT)-based segmentation method using the competitive region-growing based algorithm, implemented in the free and public available 3D-Slicer software platform. We compared the 3D-Slicer segmented volumes by three independent observers, who segmented the primary tumour of 20 NSCLC patients twice, to manual slice-by-slice delineations of five physicians. Furthermore, we compared all tumour contours to the macroscopic diameter of the tumour in pathology, considered as the “gold standard”. The 3D-Slicer segmented volumes demonstrated high agreement (overlap fractions > 0.90), lower volume variability (p = 0.0003) and smaller uncertainty areas (p = 0.0002), compared to manual slice-by-slice delineations. Furthermore, 3D-Slicer segmentations showed a strong correlation to pathology (r = 0.89, 95%CI, 0.81–0.94). Our results show that semiautomatic 3D-Slicer segmentations can be used for accurate contouring and are more stable than manual delineations. Therefore, 3D-Slicer can be employed as a starting point for treatment decisions or for high-throughput data mining research, such as Radiomics, where manual delineating often represent a time-consuming bottleneck

Multi-Modality Automatic Lung Tumor Segmentation Method Using Deep Learning and Radiomics

Delineation of the tumor volume is the initial and fundamental step in the radiotherapy planning process. The current clinical practice of manual delineation is time-consuming and suffers from observer variability. This work seeks to develop an effective automatic framework to produce clinically usable lung tumor segmentations. First, to facilitate the development and validation of our methodology, an expansive database of planning CTs, diagnostic PETs, and manual tumor segmentations was curated, and an image registration and preprocessing pipeline was established. Then a deep learning neural network was constructed and optimized to utilize dual-modality PET and CT images for lung tumor segmentation. The feasibility of incorporating radiomics and other mechanisms such as a tumor volume-based stratification scheme for training/validation/testing were investigated to improve the segmentation performance. The proposed methodology was evaluated both quantitatively with similarity metrics and clinically with physician reviews. In addition, external validation with an independent database was also conducted. Our work addressed some of the major limitations that restricted clinical applicability of the existing approaches and produced automatic segmentations that were consistent with the manually contoured ground truth and were highly clinically-acceptable according to both the quantitative and clinical evaluations. Both novel approaches of implementing a tumor volume-based training/validation/ testing stratification strategy as well as incorporating voxel-wise radiomics feature images were shown to improve the segmentation performance. The results showed that the proposed method was effective and robust, producing automatic lung tumor segmentations that could potentially improve both the quality and consistency of manual tumor delineation

Reducing Tumour Volume Uncertainty for the Benefit of Radiation Therapy Cancer Patients

The efficacy of radiotherapy is dependent on its precision and accuracy. Increasingly conformal, modulated radiation fields can be reproducibly delivered to small, complex volumes within the human body. However, treatment is not without uncertainty. This thesis focuses on limitations in radiotherapy accuracy due to uncertainty in delineation of the volume requiring treatment

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51  ±  1.92) to (97.27  ±  0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development

Evaluation of Image Registration Accuracy for Tumor and Organs at Risk in the Thorax for Compliance With TG 132 Recommendations

Purpose To evaluate accuracy for 2 deformable image registration methods (in-house B-spline and MIM freeform) using image pairs exhibiting changes in patient orientation and lung volume and to assess the appropriateness of registration accuracy tolerances proposed by the American Association of Physicists in Medicine Task Group 132 under such challenging conditions via assessment by expert observers. Methods and Materials Four-dimensional computed tomography scans for 12 patients with lung cancer were acquired with patients in prone and supine positions. Tumor and organs at risk were delineated by a physician on all data sets: supine inhale (SI), supine exhale, prone inhale, and prone exhale. The SI image was registered to the other images using both registration methods. All SI contours were propagated using the resulting transformations and compared with physician delineations using Dice similarity coefficient, mean distance to agreement, and Hausdorff distance. Additionally, propagated contours were anonymized along with ground-truth contours and rated for quality by physician-observers. Results Averaged across all patients, the accuracy metrics investigated remained within tolerances recommended by Task Group 132 (Dice similarity coefficient \u3e0.8, mean distance to agreement \u3c3 \u3emm). MIM performed better with both complex (vertebrae) and low-contrast (esophagus) structures, whereas the in-house method performed better with lungs (whole and individual lobes). Accuracy metrics worsened but remained within tolerances when propagating from supine to prone; however, the Jacobian determinant contained regions with negative values, indicating localized nonphysiologic deformations. For MIM and in-house registrations, 50% and 43.8%, respectively, of propagated contours were rated acceptable as is and 8.2% and 11.0% as clinically unacceptable. Conclusions The deformable image registration methods performed reliably and met recommended tolerances despite anatomically challenging cases exceeding typical interfraction variability. However, additional quality assurance measures are necessary for complex applications (eg, dose propagation). Human review rather than unsupervised implementation should always be part of the clinical registration workflow

Auto-Segmentation of Target Volume and Organs-at-risks for Radiotherapy in Breast Cancer patients

목적: 유방암 방사선 치료에서 치료 체적에 대한 정확한 타겟 그리기는 중요하다. 하지만 방사선 치료 계획 과정에 타겟 그리기는 의료진의 부담을 주고 있으며, 의료진 간의 편차는 존재하고 있다. 본 연구에서는 Deep learning-based auto-segmentation (DLBAS)의 성능을 atlas-based segmentation solutions (ABAS)와 비교하고, 임상 의사의 관점에서 유용성을 평가하고, 최종적으로 외부 타당도 조사를 통하여 유방암 방사선 치료에서 자동 구획화의 가능성을 규명하고자 한다. 대상 및 방법: 유방암 방사선 치료 체적과 정상장기들에 대하여 한 명의 연구진에 의하여 구획화 정보를 생성하였다. Convolutional neural network 알고리즘을 이용하여 auto-contours를 생성하였고, Dice similarity coefficient (DSC) and 95% Hausdorff distance (HD)를 이용하여 ABAS와 비교하였다. DLBAS에 의해 생성된 auto-contours의 질적인 평가를 조사하였고, manual contours와 방사선 치료 선량-체적 히스토그램을 비교하여 주요 선량평가분석을 시행하였다. 마지막으로 2개 기관의 11명의 전문가에게 manual contour를 그릴 것을 요청하여 데이터를 수집하였다. 외부 위원회를 통해 가장 최적의 치료 체적을 선정하였고, 나머지 10명의 contour와 DLBAS에 의해 생성된 auto-contour의 성능을 비교하여 순위 평가를 시행하였다. 결과: 제안된 DLBAS 모델은 대부분의 체적 (특히, 치료 체적과 심장 세부구조)에서 ABAS보다 더 일관된 결과와 높은 DSC와 낮은 HD 결과 값을 보였다. ABAS는 연조직의 정상장기와 조영제를 쓰지 않은 새로운 데이터 셋에서 DLBAS에 비해, 제한적인 성능을 보였다. 질적 평가를 위한 설문조사가 시행되었고, 중위수 8점으로 manual contour와 auto-contour 사이의 차이가 크지 않다고 대답하였으며, 임상에서 도움이 될 것으로 답변하였다. 또한 선량평가 분석 결과에서 차이는 미미하였다. 외부 검증 결과, 9개의 정상장기를 그리는데 평균 37분이 걸렸고, DLBAS는 6분이 걸렸다. Auto-contour는 전체 12개 중 1위 manual contour와 비교하였을 때 가장 DSC상 차이가 적었으며, HSD상 2번째로 차이가 적었다. 정상장기에서 가장 편차가 높았던 부위는 유방이었다. 결론: 유방 방사선 치료 계획에서 DLBAS의 실현가능성은 이번 연구에서 다각도로 검증되었다. 의료진의 최종 수정 과정은 필수적이지만, 앞으로 DLBAS는 방사선 치료를 도울 수 있는 훌륭한 가능성을 보여주었다.open박