In brain tumor surgery, the quality and safety of the procedure can be impacted by intra-operative tissue deformation, called brain shift. Brain shift can move the surgical targets and other vital structures such as blood vessels, thus invalidating the presurgical plan. Intra-operative ultrasound (iUS) is a convenient and cost-effective imaging tool to track brain shift and tumor resection. Accurate image registration techniques that update pre-surgical MRI based on iUS are crucial but challenging. The MICCAI Challenge 2018 for Correction of Brain shift with Intra-Operative UltraSound (CuRIOUS2018) provided a public platform to benchmark MRI-iUS registration algorithms on newly released clinical datasets. In this work, we present the data, setup, evaluation, and results of CuRIOUS 2018, which received 6 fully automated algorithms from leading academic and industrial research groups. All algorithms were first trained with the public RESECT database, and then ranked based on a test dataset of 10 additional cases with identical data curation and annotation protocols as the RESECT database. The article compares the results of all participating teams and discusses the insights gained from the challenge, as well as future work.

. Significance: The diagnosis of prostate cancer (PCa) and focal treatment by brachytherapy are limited by the lack of precise intraoperative information to target tumors during biopsy collection and radiation seed placement. Image-guidance techniques could improve the safety and diagnostic yield of biopsy collection as well as increase the efficacy of radiotherapy. Aim: To estimate the accuracy of PCa detection using in situ Raman spectroscopy (RS) in a pilot in-human clinical study and assess biochemical differences between in vivo and ex vivo measurements. Approach: A new miniature RS fiber-optics system equipped with an electromagnetic (EM) tracker was guided by trans-rectal ultrasound-guided imaging, fused with preoperative magnetic resonance imaging to acquire 49 spectra in situ ( in vivo ) from 18 PCa patients. In addition, 179 spectra were acquired ex vivo in fresh prostate samples from 14 patients who underwent radical prostatectomy. Two machine-learning models were trained to discriminate cancer from normal prostate tissue from both in situ and ex vivo datasets. Results: A support vector machine (SVM) model was trained on the in situ dataset and its performance was evaluated using leave-one-patient-out cross validation from 28 normal prostate measurements and 21 in-tumor measurements. The model performed at 86% sensitivity and 72% specificity. Similarly, an SVM model was trained with the ex vivo dataset from 152 normal prostate measurements and 27 tumor measurements showing reduced cancer detection performance mostly attributable to spatial registration inaccuracies between probe measurements and histology assessment. A qualitative comparison between in situ and ex vivo measurements demonstrated a one-to-one correspondence and similar ratios between the main Raman bands (e.g., amide I-II bands, phenylalanine). Conclusions: PCa detection can be achieved using RS and machine learning models for image-guidance applications using in situ measurements during prostate biopsy procedures.

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For newborns and neonates, ultrasound (US) is the most common imaging modality used for examinations due to its accessibility and ease of use. However, precise volume measurements remain limited in 2D, while MRI in newborns is typically avoided because of immobilization issues which may require sedation. The objective of this study is to assess and validate the lateral ventricular and total brain volumes obtained with an automatic segmentation method using cerebral trans-fontanelle 3D US. Infants aged between 2 and 8.5 months old were recruited, with both MRI and 3D US acquired on the same day was used to validate ventricular and brain volume measurements in comparison to MRI. Lateral ventricles were segmented on both the US (manually and with a proposed automatic fusion-based approach) and MRI, while brain volumes were estimated with an automatic segmentation method. Volumetric 3D US measurements were then evaluated with respect to age distribution. For the comparison between MRI and 3D US, strong inter-class correlations (ICC) were found for the ventricle volumes (manual: 5.9%  ±  2.5% difference (ICC  =  0.99); automatic: 6.0%  ±  2.6% difference (ICC  =  0.98)), as well as the total brain size, with a 3.0%  ±  1.3% difference (ICC  =  0.98). There was no statistically significant difference based on t-test and f-test for the lateral ventricles volume (t-test: p  =  0.542) and (f-test: p  =  0.738) and for the total brain volume (t-test: p  =  0.412) and (f-test: p  =  0.685) between MRI and 3D US. This study demonstrates that 3D US can be used to automatically assess lateral ventricular and total brain volumes with no significant difference to the MRI acquisitions. The highest correlations were obtained for infants under 8 months when the fontanelle is open.

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. Significance: The diagnosis and treatment of prostate cancer (PCa) are limited by a lack of intraoperative information to accurately target tumors with needles for biopsy and brachytherapy. An innovative image-guidance technique using optical devices could improve the diagnostic yield of biopsy and efficacy of radiotherapy. Aim: To evaluate the performance of multimodal PCa detection using biomolecular features from in-situ Raman spectroscopy (RS) combined with image-based (radiomics) features from multiparametric magnetic resonance images (mpMRI). Approach: In a prospective pilot clinical study, 18 patients were recruited and underwent high-dose-rate brachytherapy. Multimodality image fusion (preoperative mpMRI with intraoperative transrectal ultrasound) combined with electromagnetic tracking was used to navigate an RS needle in the prostate prior to brachytherapy. This resulting dataset consisted of Raman spectra and co-located radiomics features from mpMRI. Feature selection was performed with the constraint that no more than 10 features were retained overall from a combination of inelastic scattering spectra and radiomics. These features were used to train support vector machine classifiers for PCa detection based on leave-one-patient-out cross-validation. Results: RS along with biopsy samples were acquired from 47 sites along the insertion trajectory of the fiber-optics needle: 26 were confirmed as benign or grade , and 21 as grade group , according to histopathological reports. The combination of the fingerprint region of the RS and radiomics showed an accuracy of 83% ( and a ), outperforming by more than 9% models trained with either spectroscopic or mpMRI data alone. An optimal number of features was identified between 6 and 8 features, which have good potential for discriminating grade group ( ) or grade group ( ). Conclusions: In-situ Raman spectroscopy combined with mpMRI radiomics features can lead to highly accurate PCa detection for improved in-vivo targeting of biopsy sample collection and radiotherapy seed placement.

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Diagnosing unclear lymphadenopathy such as lymph node metastasis has an enormous impact on the treatment of neoplastic diseases. Therefore, the aim of treatment usually switches from cure to palliation. Numerous other diseases are accompanied by lymphadenopathy, including benign conditions such as infections and inflammatory disease. 1 Hence, sampling of the target node is often required to unveil the underlying diagnosis and to enable adequate clinical management. 2 Intrathoracic or intraabdominal lymphadenopathies are clinically challenging because the nodes are difficult to sample using non-invasive techniques. Therefore, invasive methods such as mediastinoscopy or laparotomy are needed but are associated with risks. 3 A mini-invasive method such as endosonography (EUS) is an alternative approach in such scenarios. 4 Endosonographic characteristics of malignant lymph nodes include large size, hypoechogenicity, distinct border, round shape, and high tissue stiffness on elastography. 5,6 Neverthe