Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

Spectrum of injuries associated with paediatric ACL tears: an MRI pictorial review

OBJECTIVE: Magnetic resonance imaging (MRI) findings in anterior cruciate ligament (ACL) injury are well known, but most published reviews show obvious examples of associated injuries and give little focus to paediatric patients. Here, we demonstrate the spectrum of MRI appearances at common sites of associated injury in adolescents with ACL tears, emphasising age-specific issues. METHODS: Pictorial review using images from children with surgically confirmed ACL tears after athletic injury. RESULTS: ACL injury usually occurs with axial rotation in the valgus near full extension. The MRI findings can be obvious and important to management (ACL rupture), subtle but clinically important (lateral meniscus posterior attachment avulsion), obvious and unimportant to management (femoral condyle impaction injury), or subtle and possibly important (medial meniscocapsular junction tear). Paediatric-specific issues of note include tibial spine avulsion, normal difficulty visualising a thin ACL and posterolateral corner structures, and differentiation between incompletely closed physis and impaction fracture. CONCLUSION: ACL tear is only the most obvious sign of a complex injury involving multiple structures. Awareness of the spectrum of secondary findings illustrated here and the features distinguishing them from normal variation can aid in accurate assessment of ACL tears and related injuries, enabling effective treatment planning and assessment of prognosis. TEACHING POINTS: • The ACL in children normally appears thin or attenuated, while thickening and oedema suggest tear. • Displaced medial meniscal tears are significantly more common later post-injury than immediately. • The meniscofemoral ligaments merge with the posterior lateral meniscus, complicating tear assessment. • Tibial plateau impaction fractures can be difficult to distinguish from a partially closed physis. • Axial MR sequences are more sensitive/specific than coronal for diagnosis of medial collateral ligament (MCL) injury

End-to-end detection-segmentation network with ROI convolution

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201

MRI of the axial skeleton in spondyloarthritis : the many faces of new bone formation

Spondyloarthritis has two hallmark features: active inflammation and structural lesions with new bone formation. MRI is well suited to assess active inflammation, but there is increasing interest in the role of structural lesions at MRI. Recent MRI studies have examined the established features of new bone formation and demonstrated some novel features which show diagnostic value and might even have potential as possible markers of disease progression. Although MRI is not the first imaging modality that comes into mind for assessment of bony changes, these features of new bone formation can be detected on MRI-if one knows how to recognize them. This review illustrates the MRI features of new bone formation and addresses possible pitfalls

Incidence and Significance of Inconclusive Results in Ultrasound for Appendicitis in Children and Teenagers

AbstractPurposeFrustratingly, sonography to assess for appendicitis in children often leads to an inconclusive report (eg, “suspicious for appendicitis”) or nonvisualization of the appendix. To aid in planning who to image and how to interpret the results, we investigated whether these 2 results were more frequent in teenagers than preteens and the prevalence of appendicitis associated with each result.MethodsWe retrospectively reviewed sonographic and surgical findings in patients <18 years (n = 189) referred with clinical suspicion of appendicitis over a 12-month period. Children (≤12.0 years old; n = 86) and teens (>12.0 years old; n = 103) were compared.ResultsPrevalence of appendicitis was 34% in each group, similar to other centres; 0% for those with negative ultrasound reports (0/35), 10% for nonvisualized appendix (8/84), 68% for inconclusive report (15/22), and 85% for positive ultrasound (41/48). Teens were significantly more likely to have an inconclusive ultrasound. Inconclusive reports were because of borderline findings (eg, appendix size near 6 mm; 9/22), body habitus, bowel gas, or unusual findings due in retrospect to perforation. The rate of nonvisualization of the appendix did not vary significantly with age (42% vs 47%).ConclusionAn inconclusive result of ultrasound for appendicitis was significantly more frequent in teens than in preteens and carried a high (68%) likelihood of appendicitis. Conversely, a nonvisualized appendix was equally frequent in teens and preteens, and had a low likelihood of appendicitis (only 10% positive). These findings encourage the use of ultrasound in preteens in particular and can assist interpretation of these common results

BMI1 regulates PRC1 architecture and activity through homo- and hetero-oligomerization.

BMI1 is a core component of the polycomb repressive complex 1 (PRC1) and emerging data support a role of BMI1 in cancer. The central domain of BMI1 is involved in protein-protein interactions and is essential for its oncogenic activity. Here, we present the structure of BMI1 bound to the polyhomeotic protein PHC2 illustrating that the central domain of BMI1 adopts an ubiquitin-like (UBL) fold and binds PHC2 in a beta-hairpin conformation. Unexpectedly, we find that the UBL domain is involved in homo-oligomerization of BMI1. We demonstrate that both the interaction of BMI1 with polyhomeotic proteins and homo-oligomerization via UBL domain are necessary for H2A ubiquitination activity of PRC1 and for clonogenic potential of U2OS cells. Here, we also emphasize need for joint application of NMR spectroscopy and X-ray crystallography to determine the overall structure of the BMI1-PHC2 complex

Diagnositic value of pelvic enthesitis on MRI of the sacroiliac joints in enthesitis related arthritis

Background: To determine the prevalence and diagnostic value of pelvic enthesitis on MRI of the sacroiliac (SI) joints in enthesitis related arthritis (ERA). Methods: We retrospectively studied 143 patients aged 6-18 years old who underwent MRI of the SI joints for clinically suspected sacroiliitis between 2006-2014. Patients were diagnosed with ERA according to the International League of Associations for Rheumatology (ILAR) criteria. All MRI studies were reassessed for the presence of pelvic enthesitis, which was correlated to the presence of sacroiliitis on MRI and to the final clinical diagnosis. The added value for detection of pelvic enthesitis and fulfilment of criteria for the diagnosis of ERA was studied. Results: Pelvic enthesitis was seen in 23 of 143 (16 %) patients. The most commonly affected sites were the entheses around the hip (35 % of affected entheses) and the retroarticular interosseous ligaments (32 % of affected entheses). MRI showed pelvic enthesitis in 21 % of patients with ERA and in 13 % of patients without ERA. Pelvic enthesitis was seen on MRI in 7/51 (14 %) patients with clinically evident enthesitis, and 16/92 (17 %) patients without clinically evident enthesitis. In 7 of 11 ERA-negative patients without clinical enthesitis but with pelvic enthesitis on MRI, the ILAR criteria could have been fulfilled, if pelvic enthesitis on MRI was included in the criteria. There is a high correlation between pelvic enthesitis and sacroiliitis, with sacroiliitis present in 17/23 (74 %) patients with pelvic enthesitis. Conclusions: Pelvic enthesitis may be present in children with or without clinically evident peripheral enthesitis. There is a high correlation between pelvic enthesitis and sacroiliitis on MRI of the sacroiliac joints in children. As pelvic enthesitis indicates active inflammation, it may play a role in assessment of the inflammatory status. Therefore, it should be carefully sought and noted by radiologists examining MRI of the sacroiliac joints in children

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification