Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images

Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information of tumor spread. Such information improves cancer treatment in many ways. One important usage of PET-CT in cancer treatment is to facilitate radiotherapy planning, for the information it provides helps radiation oncologists to better target the tumor region. However, currently most tumor delineations in radiotherapy planning are performed by manual segmentation, which consumes a lot of time and work. Most computer-aided algorithms need a knowledgeable user to locate roughly the tumor area as a starting point. This is because, in PET-CT imaging, some tissues like heart and kidney may also exhibit a high level of activity similar to that of a tumor region. In order to address this issue, a novel co-segmentation method is proposed in this work to enhance the accuracy of tumor segmentation using PET-CT, and a localization algorithm is developed to differentiate and segment tumor regions from normal regions. On a combined dataset containing 29 patients with lung tumor, the combined method shows good segmentation results as well as good tumor recognition rate

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

Impact of 90Y PET gradient-based tumor segmentation on voxel-level dosimetry in liver radioembolization

Abstract Background The purpose was to validate 90Y PET gradient-based tumor segmentation in phantoms and to evaluate the impact of the segmentation method on reported tumor absorbed dose (AD) and biological effective dose (BED) in 90Y microsphere radioembolization (RE) patients. A semi-automated gradient-based method was applied to phantoms and patient tumors on the 90Y PET with the initial bounding volume for gradient detection determined from a registered diagnostic CT or MR; this PET-based segmentation (PS) was compared with radiologist-defined morphologic segmentation (MS) on CT or MRI. AD and BED volume histogram metrics (D90, D70, mean) were calculated using both segmentations and concordance/correlations were investigated. Spatial concordance was assessed using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). PS was repeated to assess intra-observer variability. Results In phantoms, PS demonstrated high accuracy in lesion volumes (within 15%), AD metrics (within 11%), high spatial concordance relative to morphologic segmentation (DSC > 0.86 and MDA  0.99, MDA < 0.2 mm, AD/BED metrics within 2%). For patients (58 lesions), spatial concordance between PS and MS was degraded compared to in-phantom (average DSC = 0.54, average MDA = 4.8 mm); the average mean tumor AD was 226 ± 153 and 197 ± 138 Gy, respectively for PS and MS. For patient AD metrics, the best Pearson correlation (r) and concordance correlation coefficient (ccc) between segmentation methods was found for mean AD (r = 0.94, ccc = 0.92), but worsened as the metric approached the minimum dose (for D90, r = 0.77, ccc = 0.69); BED metrics exhibited a similar trend. Patient PS showed low intra-observer variability (average DSC = 0.81, average MDA = 2.2 mm, average AD/BED metrics within 3.0%). Conclusions 90Y PET gradient-based segmentation led to accurate/robust results in phantoms, and showed high concordance with MS for reporting mean tumor AD/BED in patients. However, tumor coverage metrics such as D90 exhibited worse concordance between segmentation methods, highlighting the need to standardize segmentation methods when reporting AD/BED metrics from post-therapy 90Y PET. Estimated differences in reported AD/BED metrics due to segmentation method will be useful for interpreting RE dosimetry results in the literature including tumor response data.https://deepblue.lib.umich.edu/bitstream/2027.42/146544/1/40658_2018_Article_230.pd

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Development and assessment of estimate methods for internal dosimetry using PET/CT

The aim of this thesis was to assess and develop internal dose calculations methods in diagnostic and therapeutic nuclear medicine procedures to patients undergone PET/CT explorations. Towards this objective, the accuracy and precision of different classical methods commonly used to estimate internal dosimetry were investigated. Biodistribution studies were used in order to compare these methods. The main study aspects included region-of-interest (ROI) delineation methods, reconstruction algorithms, scatter correction and radiopharmaceutical's biokinetic. Optimization of internal dosimetry in this thesis was completed with the development of a Monte Carlo (MC) technique for estimating the patient-specific PET/CT dosimetry. The development of a mathematical model using MC techniques allowed us to have a gold standard to which compare classical techniques and study the aspects discussed previously. It was observed that effective dose (ED) estimations were sensitive to whichever delineation ROI method was applied. Furthermore, it was perceived that the biokinetics of the radioligand also influences in the ED estimation. On the other hand, similar quantitative accuracy was found regarding image reconstruction (FBP and OSEM) and scatter correction methods studied (FSC and SSC). Analysis of the impact of inter- and intra-operator variability in dose estimations revealed higher reproducibility in 3D methods in comparison with 2D planar method. The last one, showed the highest interoperator variability, which implies an overestimation of the ED. In this dissertation, specific routines were developed to be applied with the MC code PENELOPE/penEasy to perform individualized internal dosimetry estimations. Voxel-level absorbed dose maps which include self- and cross-irradiation doses were generated from the morphological and functional patient images. Further parameters such as cumulative organ dose, maximum and minimum voxel organ values, volume of the organ and dose-volume histograms of interest were reported. The model implemented was applied to a theoretical study using simulated PET images of a voxelized Zubal phantom. The results were benchmarked with the ones obtained using the OLINDA/EXM software. The comparison was in good agreement for those organs were both phantoms considered (Zubal and the reference one in OLINDA/EXM) were close. Undoubtedly, the implementation of a patient-specific internal dosimetry method not only leads to an improvement in diagnostic examinations where the risk could be quantified, but also NM therapy could become more effective in terms that patients receiving an optimal care.L'objectiu d'aquesta tesi va ser avaluar i desenvolupar mètodes de càlcul de dosis interna en procediments de diagnòstic i terapèutics de medicina nuclear per a pacients sotmesos a exploracions PET / TC. Amb aquest objectiu, es va investigar l'exactitud i la precisió dels diferents mètodes clàssics utilitzats habitualment per estimar la dosimetria interna. Es van utilitzar estudis de biodistribució per comparar aquests mètodes. Els principals aspectes d'estudi incloïen mètodes de delimitació de la regió d'interès (ROI), algoritmes de reconstrucció, correcció de dispersió i biocinètiques de radiofàrmacs. L'optimització de la dosimetria interna en aquesta tesi es va completar amb el desenvolupament d'una tècnica de Monte Carlo (MC) per a estimar la dosimetria PET / TC específica del pacient. El desenvolupament d'un model matemàtic amb tècniques de MC ens va permetre tenir una referència amb la que comparar les tècniques clàssiques i estudiar els aspectes descrits anteriorment. Es va observar que les estimacions de la dosi efectiva (DE) eren sensibles a qualsevol mètode de delimitació de la ROI aplicada. A més a més, es va percebre que la biocinètica del radiolligand també influeix en l'estimació de la DE. D'altra banda, es va trobar una exactitud quantitativament similar pel que fa a la reconstrucció d'imatges (FBP i OSEM) i els mètodes de correcció de dispersió estudiats (FSC i SSC). L'anàlisi de l'impacte de la variabilitat entre operadors i intra-operadors en les estimacions de dosis va mostrar una major reproductibilitat en els mètodes 3D en comparació amb el mètode planar 2D. Aquest últim, va mostrar la màxima variabilitat entre operadors, la qual cosa implica una sobreestimació de la DE. En aquesta tesi, es van desenvolupar rutines específiques per aplicar-les amb el codi MC PENELOPE / penEasy per a realitzar estimacions de dosimetria interna individualitzades. Es van generar mapes de dosis absorbida a nivell de voxel que incloïen dosis d? autoirradiació i irradiació creuada a partir de les imatges morfològiques i funcionals del pacient. Es van reportar altres paràmetres d?interès com la dosi d'òrgan acumulada, els valors màxims i mínims de l'òrgan i del vòxel, el volum de l'òrgan i els histogrames de dosi-volum. El model implementat es va aplicar a un estudi teòric mitjançant imatges simulades de PET d'un maniquí de Zubal voxelitzat. Els resultats es van comparar amb els obtinguts mitjançant el programa OLINDA / EXM. Es va observar un bon acord per a aquells òrgans semblants entre el maniquí de Zubal i el maniquí de referència del software OLINDA/EXM. Sens dubte, la implementació d'un mètode de dosimetria interna específic per al pacient no només condueix a una millora en les exploracions de diagnòstic on es pot quantificar el risc d?irradiació, sinó que la teràpia amb medicina nuclear podria ser més eficaç en termes que els pacients rebin un tractament òptim.Postprint (published version