U-Net Modelling-Based Imaging MAP Score for Tl Stage Nephrectomy: An Exploratory Study

We evaluate the stability of the clinical application of the MAP scoring system based on anatomical features of renal tumour images, explore the relevance of this scoring system to the choice of surgical procedure for patients with limited renal tumours, and investigate the effectiveness of automated segmentation and reconstruction 3D models of renal tumour images based on U-net for interpretative cognitive navigation during laparoscopy Tl stage radical renal tumour cancer surgery. A total of 5 000 kidney tumour images containing manual annotations were applied to the training set, and a stable and efficient full CNN algorithm model oriented to clinical needs was constructed to regionalism and multistructure and to finely automate segmentation of kidney tumour images, output modelling information in STL format, and apply a tablet computer to intraoperatively display the Tl stage kidney tumour model for cognitive navigation. Based on a training sample of MR images from 201 patients with stage Tl renal tumour cancer, an adaptation of the classical U-net allows individual segmentation of important structures such as renal tumours and 3D visualisation to visualise the structural relationships and the extent of tumour invasion at key surgical sites. The preoperative CT and clinical data of 225 patients with limited renal tumours treated surgically at our hospital from August 2011 to August 2012 were retrospectively analysed by three imaging physicians using the MAP scoring system for the total score and the variables R (maximum diameter), E (exogenous/endogenous), N (distance from the renal sinus), A (ventral/dorsal), L (relationship along the longitudinal axis of the kidney), and h (whether in contact with the renal hilum). The score for each variable (contact with the renal hilum) was statistically compared with each other for the three observers. Patients were divided into three groups according to the total score—low, medium, and high—and according to the surgical procedure—radical and partial resection. The correlation between the total score and the score of each variable and the choice of surgical procedure was analysed. The agreement rate of the total score and the score of each variable for all three observers was over 90% ( P ≤ 0.001). The map scoring system based on the anatomical features of renal tumour imaging was well stabilized, and the scores were significantly correlated with the surgical approach.

Renal arteriovenous malformation and venous thrombosis: a tumour-like presentation

Renal arteriovenous malformation is a primarily congenital renal vascular abnormality. It is usually diagnosed incidentally on imaging, and the most common subtype is ‘cirsoid’, consisting of multiple, enlarged arterial feeders interconnecting with draining veins. We present a 74-year-old woman with an incidental finding of what was at first considered a hypervascularised kidney tumour but turned out to be a left intrarenal arteriovenous malformation associated with a left renal vein thrombosis. Selective endovascular embolisation was performed. The cause-consequence relationship between the arteriovenous malformation and the thrombosis is unique. To our knowledge, no such case has ever been reported.

WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

Background: Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation.Methods: We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity.Results: We trained and evaluated the model with CT images from 300 patients. The findings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions: The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

WP-UNet: Weight Pruning U-Net with Depth-wise Separable Convolutions for Semantic Segmentation of Kidney Tumours

Background Accurate semantic segmentation of kidney tumours in computed tomography (CT) images is difficult because tumours feature varied forms and, occasionally, look alike. The KiTs19 challenge sets the groundwork for future advances in kidney tumour segmentation. Methods We present WP-UNet, a deep network model that is lightweight with a small scale; it involves few parameters with a quick assumption time and a low floating-point computational complexity. Results We trained and evaluated the model with CT images from 300 patients. Thefindings implied the dominance of our method on the training Dice score (0.98) for the kidney tumour region. The proposed model only uses 1,297,441 parameters and 7.2e FLOPS, three times lower than those for other network models. Conclusions The results confirm that the proposed architecture is smaller than that of U-Net, involves less computational complexity, and yields good accuracy, indicating its potential applicability in kidney tumour imaging.

KidNet

Kidney cancer is one of the 10 most common cancers in both men and women. The lifetime risk for one developing kidney cancer is about 1.6%. The rate of kidney cancer diagnosis has been rising since the 1990s due to the use of newer imaging tests such as CT scans. The kidneys are deep inside the body and hence small kidney tumours cannot be seen or felt during a physical examination. Existing work on kidney tumour diagnosis uses traditional machine learning and image processing techniques to find and classify the images. Deep learning systems do not require this domain-specific knowledge. The kidney tumour diagnosis system uses deep learning and convolutional neural networks to classify CT images. A deep learning neural network model named KidNet has been implemented. It has been trained using labelled kidney CT images. To achieve acceleration during the training phase, GPUs have been used. The network when trained with abdominal CT images achieved 86.1% accuracy, and the one trained with cropped portion of kidney images achieved 89.6% accuracy.

Deep Learning for Medical Image Segmentation

Pixel accurate 2-D, 3-D medical image segmentation to identify abnormalities for further analysis is on high demand for computer-aided medical imaging applications. Various segmentation algorithms have been studied and applied in medical imaging for many years, but the problem remains challenging due to growing a large number of variety of applications starting from lung disease diagnosis based on x-ray images, nucleus detection, and segmentation based on microscopic pictures to kidney tumour segmentation. The recent innovation in deep learning brought revolutionary advances in computer vision. Image segmentation is one such area where deep learning shows its capacity and improves the performance by a larger margin than its successor. This chapter overviews the most popular deep learning-based image segmentation techniques and discusses their capabilities and basic advantages and limitations in the domain of medical imaging.

Gigantic kidney tumour presenting with weight gain

A 61 years obese gentleman presented early with gain of weight and lump in the left side of abdomen for 15 days. On contrast enhanced computed tomography (CECT) of abdomen, a giant renal mass arising from left kidney. Patient underwent open nephrectomy, surgically removed en bloc of 12.5 kg weight largest renal mass. Histopathology showed papillary renal cell carcinoma. The postoperative period was uneventful.