A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Imaging of Anal Squamous Cell Carcinoma: Survey Results and Expert Opinion from the Rectal and Anal Cancer Disease-Focused Panel of the Society of Abdominal Radiology.

The role and method of image-based staging of anal cancer has evolved with the rapid development of newer imaging modalities and the need to address the rising incidence of this rare cancer. In 2014, the European Society of Medical Oncology mandated pelvic magnetic resonance imaging (MRI) for anal cancer and subsequently other societies such as the National Comprehensive Cancer Network followed suit with similar recommendations. Nevertheless, great variability exists from center to center and even within individual centers. Notably, this is in stark contrast to the imaging of the anatomically nearby rectal cancer. As participating team members for this malignancy, we embarked on a comprehensive literature review of anal cancer imaging to understand the relative merits of these new technologies which developed after computed tomography (CT), e.g., MRI and positron emission tomography/computed tomography (PET/CT). The results of this literature review helped to inform our next stage: questionnaire development regarding the imaging of anal cancer. Next, we distributed the questionnaire to members of the Society of Abdominal Radiology (SAR) Rectal and Anal Disease-Focused Panel, a group of abdominal radiologists with special interest, experience, and expertise in rectal and anal cancer, to provide expert radiologist opinion on the appropriate anal cancer imaging strategy. In our expert opinion survey, experts advocated the use of MRI in general (65% overall and 91-100% for primary staging clinical scenarios) and acknowledged the superiority of PET/CT for nodal assessment (52-56% agreement for using PET/CT in primary staging clinical scenarios compared to 30% for using MRI). We therefore support the use of MRI and PET and suggest further exploration of PET/MRI as an optimal combined evaluation. Our questionnaire responses emphasized the heterogeneity in imaging practice as performed at numerous academic cancer centers across the United States and underscore the need for further reconciliation and establishment of best imaging practice guidelines for optimized patient care in anal cancer

The performance of PI-RADSv2 and quantitative apparent diffusion coefficient for predicting confirmatory prostate biopsy findings in patients considered for active surveillance of prostate cancer

International audienceTo assess the performance of the updated Prostate Imaging Reporting and Data System (PI-RADSv2) and the apparent diffusion coefficient (ADC) for predicting confirmatory biopsy results in patients considered for active surveillance of prostate cancer (PCA)

Meaningful words in rectal MRI synoptic reports: How “polypoid” may be prognostic

PurposeThis study explored the clinicopathologic outcomes of rectal tumor morphological descriptors used in a synoptic rectal MRI reporting template and determined that prognostic differences were observed.MethodsThis retrospective study was conducted at a comprehensive cancer center. Fifty patients with rectal tumors for whom the synoptic descriptor "polypoid" was chosen by three experienced radiologists were compared with ninety comparator patients with "partially circumferential" and "circumferential" rectal tumors. Two radiologists re-evaluated all cases. The outcome measures were agreement among two re-interpreting radiologists, clinical T staging with MRI (mrT) and descriptive nodal features, and degrees of wall attachment of tumors (on MRI) compared with pathological (p) T and N stage when available.ResultsRe-evaluation by two radiologists showed moderate to excellent agreement in tumor morphology, presence of a pedicle, and degree of wall attachment (k = 0.41-0.76) and excellent agreement on lymph node presence and size (ICC = 0.83-0.91). Statistically significant lower mrT stage was noted for polypoid morphology, wherein 98% were mrT1/2, while only 7% and 2% of partially circumferential and circumferential tumors respectively were mrT1/2. Pathologic T and N stages among the three morphologies also differed significantly, with only 14% of polypoid cases higher than stage pT2 compared to 48% of partially circumferential cases and 60% of circumferential cases.ConclusionUsing a "polypoid" morphology in rectal cancer MRI synoptic reports revealed a seemingly distinct phenotype with lower clinical and pathologic T and N stages when compared with alternative available descriptors.Precis"Polypoid" morphology in rectal cancer confers a lower clinical and pathologic T and N stage and may be useful in determining whether to proceed with surgery versus neoadjuvant treatment

Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation

PURPOSE:To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. METHODS:This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II-III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. RESULTS:Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). CONCLUSIONS:Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection